Multi-sensor systems require precise time synchronization for accurate data fusion. However, currently prevalent software time synchronization methods often rely on clocks provided by the Global Navigation Satellite System (GNSS), which may not offer high accuracy and can be easily affected by issues with GNSS signals. To address this limitation, this study introduces a novel laser pulse-driven time synchronization (LPTS) method in our custom-developed Light Detecting and Ranging (LiDAR) system. The LPTS method uses electrical pulses, synchronized with laser beams as the time synchronization source, driving the Micro-Controller Unit (MCU) timer within the control system to count with a timing accuracy of 0.1 $\mathsf{\mu }$s and to timestamp the data from the Positioning and Orientation System (POS) unit or laser scanner unit. By employing interpolation techniques, the POS and laser scanner data are precisely synchronized with laser pulses, ensuring strict correlation through their timestamps. In this article, the working principles and experimental methods of both traditional time synchronization (TRTS) and LPTS methods are discussed. We have implemented both methods on experimental platforms, and the results demonstrate that the LPTS method circumvents the dependency on external time references for inter-sensor alignment and minimizes the impact of laser jitter stemming from third-party time references, without requiring additional hardware. Moreover, it elevates the internal time synchronization resolution to 0.1 $\mathsf{\mu }$s and significantly improves relative timing precision.

Accurate modeling of lithium-ion batteries is crucial for optimizing their performance and ensuring the safety of energy storage systems. Converting electrochemical impedance spectroscopy (EIS) data under various conditions into equivalent circuit elements that reflect the battery’s physicochemical characteristics is highly valuable for energy storage research. This paper introduces a novel equivalent circuit model (ECM) that uses the distribution of relaxation times (DRT) to analyze EIS data in specific frequency ranges, thereby facilitating the selection of ECM circuit elements. The proposed ECM is suitable for wide-frequency impedance data, making it applicable to different batteries under various conditions. The Sinh Cosh Optimizer (SCHO) algorithm is employed for parameter identification of the ECM under these conditions. The root mean square error (RMSE) for all conditions in the dataset is below 0.0014Ω, with the absolute error at most frequency points under 0.0005Ω. This demonstrates the high accuracy of the SCHO algorithm, which reduces RMSE by over 25% compared to other algorithms. The ECM developed in this paper is validated against EIS data from two different battery types, NMC 21700 and LR 2032, confirming its applicability across different battery chemistries. The model is capable of accurately fitting EIS data under varying states of charge (SOC), state of health (SOH), and temperature conditions, maintaining stability and precision even at low SOC. The results highlight the model’s potential for optimizing battery management and enhancing battery performance monitoring across different conditions and battery types.

With the rapid development of the Internet of Vehicles (IoV), the explosive growth of data traffic within the system has led to a surge in demand for spectrum resources. However, the strict limitations on spectrum supply make the construction of an efficient and reasonable resource allocation scheme crucial for IoV. To maximize social benefits and improve security in the resource allocation process under IoV spectrum scarcity, this paper proposes a dynamic spectrum allocation (DSA) scheme based on a consortium blockchain framework. In this scheme, we design a demand-based vehicle priority classification method and propose a novel hybrid consensus mechanism—PhDPoR—which integrates practical byzantine fault tolerance (PBFT) and Hierarchical Delegated Proof of Reputation. Furthermore, we construct a multi-leader, multi-follower (MLMF) Stackelberg game model and utilize smart contracts to implement an immutable on-chain record of spectrum resource allocation, thereby deriving the optimal spectrum pricing and purchase strategy. Experimental results show that the proposed scheme not only effectively optimizes the utility of both supply and demand sides and improves overall social benefits while ensuring efficiency, but also significantly outperforms baseline algorithms in identifying and mitigating malicious nodes, thus verifying its feasibility and application value in complex IoV environments.

Rubber extrusion has evolved through overlapping advances in equip design, rheological characterization, numerical modeling, sensing, and control. This structured critical narrative review synthesizes 180 sources published from 1972 to 2026, assembled through iterative keyword searching and backward and forward citation tracing. The conventional three-zone theory of solid conveying, compression, and metering is used as a bounded analytical framework, while the literature is organized into four overlapping analytical periods spanning empirical design, constitutive and numerical modeling, engineering-scale simulation, and multiphysics and data-enabled methods. Evidence is distinguished among direct rubber-extrusion validation, rubber-material or rheological studies, transferable general polymer extrusion studies, and enabling computational, sensing, or control research. Five persistent challenges are identified: formulation-dependent nonlinear rheology; incomplete representation of filler-network evolution and wall slip; limited cross-machine and cross-formulation validation; high computational cost; and the lack of standardized datasets and reporting protocols. Digital twins, physics-informed neural networks, and neural operators are promising but remain insufficiently validated for industrial rubber extrusion. Priorities include transparent benchmark datasets, evidence-graded, uncertainty- and latency-aware validation, multimodal sensing, transferable reduced-order and learned models, and measurable sustainability indicators. Together, these priorities define a validation-oriented roadmap for more reliable, transferable, and sustainable rubber-extrusion modeling and control.

With the rapid development of intelligent transportation systems, the increasing number of connected vehicles and onboard sensing devices has generated substantial data traffic and placed stringent demands on communication resources in the Internet of Vehicles. In three-dimensional urban environments, physical blockage, high mobility, and co-channel interference can cause temporary link disruptions, preventing the timely dissemination of safety-critical vehicular information. This study investigates joint resource allocation for Cellular Vehicle-to-Everything (C-V2X) networks with heterogeneous vehicular platoons. We propose an Age of Information (AoI)- and Fairness-Aware Multi-Agent Resource Allocation (AF-MARL) 1 under imperfect and delayed channel state information. Its novelty lies in combining an AoI-dependent power cost with a cross-platoon AoI-dispersion penalty within a multi-task multi-agent reinforcement learning (MARL) architecture, thereby reducing unnecessary high-power transmission by favorable platoons while explicitly protecting disadvantaged platoons without adding Actor or Critic networks. Simulation results show that AF-MARL reduces interference and persistent AoI outages while maintaining reliable Cooperative Awareness Message (CAM) delivery under different platoon spacings and sizes.