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This study studies the problem of synchronisation control for spacecraft formation via extended state observer approach over directed communication topology. The attitude kinematics and dynamics of spacecraft are described by Lagrangian formulations, and the decentralised controller is designed with time-varying external disturbances and unmeasurable velocity information. In particular, the estimation of disturbances obtained via extended state observer is used for the decentralised controller design. A novel Lyapunov function is proposed to show that both static regulation and dynamic synchronisation are realised. Finally, simulation results are given to demonstrate the effectiveness of the controllers proposed in this study.

Cloud manufacturing (CM) and Internet of things (IoT) are interlinked, yet most works only focused on one of them and take the other as a constituent technology unit. This is practically inadequate, especially for a highly service-driven manufacturing execution system which entails systematical CM supports to respond to the real-time dynamics captured from the IoT-enabled execution hierarchy. To deal with the dynamics occurring in production logistics (PL) processes, this paper investigates a dynamic PL synchronization (PLS) of a manufacturer adopting public PL services. Contemporary CM and IoT infrastructures are systematically integrated to enable a smart PLS control mechanism with multi-level dynamic adaptability. The S-CM operation framework, operation logic, and PLS infrastructure are presented with an industrial case, and the effectiveness is also demonstrated and analyzed.

The increasing penetration of renewable energy leads to a continuous reduction in system inertia, for which conventional synchronization criteria based solely on frequency consistency can no longer accurately capture the coupled dynamics of frequency and voltage during transients. To address this issue, this paper employs the concept of complex frequency and develops an analysis framework that integrates theory, indices, and simulation for assessing synchronization stability in low-inertia power systems. Firstly, the basic concepts and mathematical formulation of complex frequency and complex frequency synchronization are introduced. Then, dynamic criteria for local and global complex synchronization are established, upon which a complex inertia index is proposed. This index unifies the supporting role of traditional frequency inertia and the voltage support capability associated with voltage inertia, enabling the quantitative evaluation of the strength of coordinated frequency–voltage support and disturbance rejection within a region. Finally, transient simulations on a modified WSCC nine-bus system are carried out to validate the proposed method. The results show that the method can clearly reveal the synchronization relationships between subnetworks and the overall system, providing a useful theoretical reference for stability analysis and control strategy design in low-inertia power systems.

The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%. This result supports the existence of differential brain synchronisation dynamics between typical and dyslexic seven-year-old readers. Furthermore, we have obtained interpretable representations using a novel feature mask to link the most relevant regions during classification with the cognitive processes attributed to normal reading and those corresponding to compensatory mechanisms found in dyslexia. Graphical Abstract

Understanding oscillatory behavior in human networks is essential for exploring synchronization, coordination, and collective dynamics. In this study, we investigate tempo oscillations in complex human networks using a system of coupled violin players with precisely controlled network parameters. Each player interacts via delayed auditory feedback, allowing us to explore the effects of connectivity, delay, and tempo on network oscillations. We identify two distinct types of oscillations: fast (2–3 s) and slow (5–25 s), and demonstrate that their periods are independent of network size and delay but are strongly correlated with the network’s average tempo. Additionally, we show that increasing the number of coupled neighbors enhances oscillation damping, indicating the role of connectivity in stabilizing network dynamics. By varying the delay rate, we discover a critical decay rate where oscillation amplitude transitions from damping to amplification. These results provide valuable insights into the dynamic interplay of tempo, delay, and connectivity in coupled oscillator systems, with implications for applications in group dynamics, distributed systems, and synchronization processes.

This paper addresses the fixed-time synchronization controller design problem for a class of nonlinear coupled Cohen–Grossberg neural networks (NCCGNNs) with switching parameters and time-varying delays based on synchronization dynamics analysis. First, a class of NCCGNNs with switching parameters and time-varying delays are considered. Second, according to the derived sufficient condition, a fixed-time synchronization controller is designed. Moreover, the advantages and disadvantages of the designed controller are discussed. Third, to improve the disadvantages of the obtained control method, by using synchronization dynamics analysis, some fixed-time synchronization controllers are further designed. Finally, two examples illustrate the usefulness and feasibility of the derived theoretical results.

Aiming at the cluster network composed of BA scale-free network, this paper proposes two one-way driving inter-cluster interconnection methods. One inter-cluster interconnection method is connected to the generous node of the driven sub-network, referred to as BDN mode. The other is a small node connected to the driven subnet, referred to as SDN mode. Using the second eigenvalue and the synchronization dynamics to compare the synchronization capabilities of cluster networks in different inter-cluster interconnection modes, it is found that the cluster networks interconnected by BDN mode have stronger synchronization capabilities.

The topic of this study is using the fractional-order chaotic synchronization system to identify chattering that CNC machines produce during production. The appearance of chattering indicates instability during the metal milling process which will not only cause abnormal wear on the tool, but can also decrease the precision of the work piece significantly. Thus, identification of chattering has always been a very important research topic. However, previous chattering identification mostly relied on the experience of the operator. Most past studies were based on the energy perspective. When the main frequency in the frequency domain analysis to the existing spindle rotation frequency ratio is a non-integer multiple, then chattering has occurred. We propose a brand new chattering identification method which uses the synchronization error plane centroid in fractional-order chaotic synchronization system to find chattering. Thus, we can simply use position of the chaotic centroid to determine whether or not the current cutting status has chattering. The result of this study shows that the method we proposed can effectively identify chattering and that the identification result is very accurate and useful.

This work investigates how interpersonal interactions among individuals could affect the dynamics of awareness raising. Even though previous studies on mathematical models of awareness in the decision making context demonstrate how the level of awareness results from self-observation impinged by optimal decision selections and external uncertainties, an explicit accounting of interaction among individuals is missing. Here we introduce for the first time a theoretical mathematical framework to evaluate the effect on individual awareness exerted by the interaction with neighbor agents. This task is performed by embedding the single agent model into a graph and allowing different agents to interact by means of suitable coupling functions. The presence of the network allows, from a global point of view, the emergence of diffusion mechanisms for which the population tends to reach homogeneous attractors, and, among them, the one with the highest level of awareness. The structural and behavioral patterns, such as the initial levels of awareness and the relative importance the individual assigns to their own state with respect to others’, may drive real actors to stress effective actions increasing individual and global awareness.

Dynamic criticality—the balance between order and chaos—is fundamental to genome regulation and cellular transitions. In this study, we investigate the distinct behaviors of gene expression dynamics in MCF-7 breast cancer cells under two stimuli: heregulin (HRG), which promotes cell fate transitions, and epidermal growth factor (EGF), which binds to the same receptor but fails to induce cell-fate changes. We model the system as an open, nonequilibrium thermodynamic system and introduce a convergence-based approach for the robust estimation of information-thermodynamic metrics. Our analysis reveals that the Shannon entropy of the critical point (CP) dynamically synchronizes with the entropy of the rest of the whole expression system (WES), reflecting coordinated transitions between ordered and disordered phases. This phase synchronization is driven by net mutual information scaling with CP entropy dynamics, demonstrating how the CP governs genome-wide coherence. Furthermore, higher-order mutual information emerges as a defining feature of the nonlinear gene expression network, capturing collective effects beyond simple pairwise interactions. By achieving thermodynamic phase synchronization, the CP orchestrates the entire expression system. Under HRG stimulation, the CP becomes active, functioning as a Maxwell’s demon with dynamic, rewritable chromatin memory to guide a critical transition in cell fate. In contrast, under EGF stimulation, the CP remains inactive in this strategic role, passively facilitating a non-critical transition. These findings establish a biophysical framework for cell fate determination, paving the way for innovative approaches in cancer research and stem cell therapy.