In this paper, a full-domain stochastic response analysis is performed based on the meshless method to reveal the time–frequency dynamic characteristics, including the power spectral density (PSD) responses in the frequency domain and the evolving PSD distribution in the time domain for a sandwich trapezoidal plate–shell coupled system. The general governing equations are derived based on the first-order shear deformation theory (FSDT), linear piston theory and Hamilton’s principle, and the stochastic excitation is integrated into the meshless framework based on the pseudo-excitation method (PEM). By constructing the meshless shape function covering the entire structural domain from Chebyshev polynomials and discretizing the continuous domain into a series of nodes within a square definition domain, the points are assembled according to the sequence number and the equilibrium relationship on the coupling edge to obtain the overall vibration equations. The validity is demonstrated by matching the mode shapes, PSD responses, time history displacement and critical flutter boundaries with FEM simulation and reported data. Finally, the time–frequency characteristics of each substructure under global and single stochastic excitation, and the effect of aerodynamic pressure on full-domain stochastic vibration, are revealed.

Complex cyberattacks on electric power networks traverse communication links, control objects and power-service processes, making it difficult to explain how network evidence propagates to operational impact. This paper proposes an explainable cyber range-to-MATLAB co-simulation framework that couples traffic-oriented security emulation with power service simulation. The cyber range generates security events, traffic records, alarms and asset topology evidence, while MATLAB models calculate distribution network states, line power flows, switching states, energy-storage PCS states and service-impact indicators. A unified clock, event-mapping function, asset topology and evidence graph connect cyber events with business disturbances and observable power outputs. Mathematical descriptions are developed for event sets, service-state vectors, coupling functions, attack intensity, service-impact score, synchronization error, evidence completeness and metric improvement. Validation focuses on distribution network and energy storage scenarios. At the mapped switching disturbance at t = 180 s, 23 of 33 nodes are de-energized, and 2.235 MW (60.16% of feeder load) is unsupplied. Five repeated trials with plus or minus 3% load perturbations give a mean load loss of 2.243 plus or minus 0.008 MW. Integrated defensive retesting reduces attack intensity from 0.60 to 0.08, alarm latency from 12.4 s to 2.8 s, and recovery time from 240 s to 60 s. The framework, therefore, links cyber-range evidence to direct, auditable power-service quantities for attack-chain interpretation and defensive assessment.

Power industrial control systems (ICSs) have evolved into tightly coupled cyber-physical systems, which enlarges the attack surface and makes online vulnerability verification risky for critical infrastructure. Existing software-only simulators usually abstract firmware, industrial protocols, and device-specific behavior, whereas purely physical testbeds are costly and difficult to scale. To address this gap, this paper proposes a multi-agent-driven hybrid digital twin framework for adaptive cybersecurity vulnerability verification in power cyber-physical systems. The core of the framework is a high-fidelity three-in-one verification environment that integrates physical entities, network emulation, and power-process simulation. The framework complements physical testbeds rather than replacing them: fidelity-critical controllers and source-grid-load-storage devices are retained in controlled hardware loops, while larger communication topologies and power-process contexts are virtualized. Red-team and blue-team agents then orchestrate vulnerability triggering, attack-chain replay, intrusion detection, mitigation, and impact assessment. A virtual–real cross-verification mechanism calibrates the virtual models using physical measurements and validates candidate vulnerabilities in controlled hardware loops. A wind-turbine PLC near-source attack case demonstrates how the framework links cyber events, controller behavior, and physical operating states, and the reported quantitative indicators are interpreted as scenario-specific verification logs rather than universal statistical guarantees. The framework therefore provides a safe, scalable, and evidence-driven path for vulnerability verification and resilience enhancement in power critical infrastructures.

The Automatic Milking System (AMS) has significantly improved dairy farming efficiency. However, residual vibrations generated by the high-speed motion of the milking manipulator continue to pose significant challenges, particularly by compromising positioning accuracy and cup attachment performance. This paper proposes a Closed-Loop Calibrated Digital Twin (CLCDT) framework to achieve low-cost, real-time vibration suppression. First, a dynamic model incorporating joint stiffness and damping is formulated using Lagrange mechanics. For online calibration, a Recursive Least Squares with Forgetting Factor (RLS-FF) method rapidly identifies these parameters within 18 iterations. A Deep Neural Network (DNN) surrogate model is then developed to predict residual vibration characteristics achieving dynamic response prediction errors of only 3.30% for dominant frequency and 5.54% for stabilization time. Finally, a differential evolution (DE) optimized AS-curve trajectory planner is integrated into the system. Experimental results show that compared to conventional T-type planning, the proposed method reduces residual vibration stabilization time to under 0.07 s (a maximum improvement of 57.33%). In practical cup-attachment tasks, total process time was reduced by an average of 0.52 s, and visual positioning time decreased by 48.78%. These findings validate that the CLCDT framework significantly enhances operational efficiency and accuracy.