Robotic digital twins are emerging as a transformative paradigm in robotics, enabling real-time synchronization between physical robots and high-fidelity virtual replicas. Compared with traditional simulation, robotic digital twins establish a closed-loop physical–virtual–data–service–knowledge architecture that supports lifecycle management, predictive analysis, and autonomous optimization. This review provides a comprehensive synthesis of recent progress in robotic digital twins. First, we summarize the core architecture and dynamic characteristics, highlighting multi-level multi-domain modeling and real-time bidirectional interaction. Second, we analyze the key technology stack, including data acquisition and fusion, high-fidelity model construction, anomaly prediction, and artificial intelligence (AI)-enhanced decision-making. Third, we examine representative applications in operation and maintenance contexts, personalized service, and human–robot collaboration. Finally, we discuss major challenges, such as model fidelity, synchronization performance, and standardization, and then outline future directions toward artificial intelligence-digital twin (AI-DT) symbiosis, cross-domain system integration, and human-centric ecosystems. This survey aims to serve as a reference for both academic research and industrial deployment of robotic digital twins.

Lithium metal is regarded as an ideal anode material for high-energy-density batteries due to its high theoretical capacity and low electrochemical potential. However, its practical application is hindered by unstable electrode-electrolyte interfaces and lithium dendrite growth. Covalent organic frameworks (COFs), with ordered ion transport channels and abundant lithiophilic sites, offer a promising solution by facilitating uniform lithium deposition, suppressing dendrites, and mitigating side reactions. This review presents a comprehensive overview of the application of COFs in various battery components for regulating interfacial chemistry and inhibiting dendrite growth. The design concepts and synthesis techniques of COFs are explained in detail, and the discussion of present issues and potential future research avenues is included in the end.

This study examines the feasibility of integrating advanced battery technologies into electrified propulsion systems for aviation as a pathway toward carbon emission reduction. While the successful deployment of battery-powered electric vehicles (EVs) has demonstrated the potential of electrification in sustainable mobility, the aviation sector presents distinct technical and operational challenges that require specialized engineering solutions. This work provides a comprehensive review of recent industrial developments and scholarly literature to evaluate the technological, environmental, and economic viability of electrified aircraft. Key performance limitations and energy density constraints associated with current lithium-based batteries are analyzed, along with their safety considerations and life cycle sustainability. In addition, the operational costs of battery-powered and hybrid-electric aircraft concepts are compared with those of conventional jet fuel-based systems. Recent progress in hybrid-electric propulsion architectures, emerging battery chemistries such as lithium-metal and lithium-sulfur, and future directions for propulsion system integration are also discussed. Overall, this study offers insights into sustainable aviation strategies and identifies critical research directions to accelerate the transition toward carbon-neutral flight.

Multimodal Large Language Models (MLLMs) have achieved remarkable progress across a range of vision-language tasks and demonstrate strong potential for traffic accident understanding. However, existing MLLMs in this domain primarily focus on coarse-grained image-level or video-level comprehension and often struggle to handle fine-grained visual details or localized scene components, limiting their applicability in complex accident scenarios. To address these limitations, we propose SafePLUG, a novel framework that empowers MLLMs with both pixel-level understanding and temporal grounding for comprehensive traffic accident analysis. SafePLUG supports both arbitrary-shaped visual prompts for region-aware question answering and pixel-level segmentation based on language instructions, while also enabling the recognition of temporally anchored events in traffic accident scenarios. To advance the development of MLLMs for traffic accident understanding, we curate a new dataset, SafePLUG-Bench, which contains diverse multimodal question–answer pairs with detailed pixel-level annotations and temporal event boundaries across a wide range of accident scenarios. Experimental results show that SafePLUG achieves strong performance on multiple tasks, including region-based question answering, pixel-level segmentation, temporal event localization, and accident event understanding. These capabilities lay a foundation for a fine-grained understanding of complex traffic scenes, with the potential to improve driving safety and enhance situational awareness in smart transportation systems.

With the development of vehicle-to-infrastructure (V2I) and intelligent connected vehicle technologies, roadside sensing data provides a new foundation for vehicle queue prediction at signalized intersections. However, existing queue-prediction models still have prominent limitations. Mechanism-based models generally ignore the impact of signal-phase changes on driver start–stop behaviour and do not account for individual driver differences, while data-driven models act as black boxes and rely heavily on large-scale historical data. In addition, some connected-vehicle-based approaches are constrained by low penetration rates or limited real-time performance, restricting their engineering applicability. To address these issues, this paper proposes a microscopic driver-behaviour-oriented queue prediction method for signalized intersections based on roadside traffic data. The classical intelligent driver model (IDM) is extended by introducing a traffic-light remaining-time adjustment term, so that drivers’ anticipatory braking under red phases and speed adaptation near the end of green phases are explicitly embedded in the longitudinal acceleration. Together with the minimizing overall braking induced by lane changes (MOBIL) model, a unified prediction framework is constructed to describe both car-following and lane-changing decisions. Using vehicle position, speed, and signal-phase information collected by roadside sensors, a dynamic evolution model of queue formation, stagnation, and dissipation is further established, and quantitative estimation methods are developed for the maximum queue length and queue dissipation time. Multi-scenario validation is conducted on a simulation of urban mobility (SUMO)-based platform and with field data from the Yizhuang corridor in Beijing. The proposed method achieves a mean absolute percentage error (MAPE) of 20.66% in simulation and 25.73% in field verification, and outperforms conventional IDM-based and support vector regression (SVR)-based baselines in prediction accuracy across varying traffic demand levels. These findings indicate that the proposed method can provide practical support for signal timing optimisation, green-wave coordination, and V2I-based traffic management.

Timely detection of subsurface road defects is critical for structural safety and pavement longevity. Here, we propose MARD-Net, an enhanced deep learning framework designed for the accurate identification of urban subsurface road defects. First, addressing the scarcity of defect samples, a hybrid dataset was constructed by integrating empirical data acquired via the GS8000 ground-penetrating radar (GPR) system and synthetic data generated by gprMax. Second, to address complex geological backgrounds and variations in defect waveform scales, the RepNCSPELAN4_CAA module was integrated into the architecture. By combining structural re-parameterization with context anchor attention (CAA), this module enhances feature reuse and inter-channel interaction, enabling the fine-grained discrimination of subtle defects. Third, a lightweight asymmetric detection head (LADH), incorporating depthwise separable convolution (DSConv) within its regression branch, was developed to significantly reduce computational costs while maintaining robust detection performance. Finally, to overcome weak and uneven defect signals across imaging depths, a multi-path coordinate attention (MPCA) mechanism adaptively fuses global and local contextual information for precise defect recognition. Empirical experiments show that MARD-Net achieves a 2.07% increase in mean average precision at an intersection over union threshold of 0.50 (mAP@50) over the baseline YOLOv11n while reducing floating point operations (FLOPs) by 2.0 giga floating point operations (GFLOPs).