Benefiting from the synergistic integration of aerial agility and terrestrial endurance, aerial-ground amphibious platforms can effectively traverse unstructured environments, demonstrating considerable potential for emergency response and reconnaissance applications. However, most existing systems adopt a decoupled configuration, utilizing independent propulsion units for ground and aerial modes. Although this approach enables dual-domain operation, the duplicated actuators and transmission chains inevitably incur structural redundancy and additional mass, which reduce overall system integration and payload efficiency. To address these limitations, this paper proposes a novel aerial-ground amphibious platform based on an integrated wheel-propeller structure. In the proposed design, a single electric motor actuates both the wheel and ducted propeller through a shared powertrain, where an electromagnetic clutch selectively engages the transmission path to switch between terrestrial locomotion and aerial propulsion. Building upon this mechanism, an amphibious platform configuration incorporating eight-wheel-propeller units is developed. By establishing a dynamic simulation model of the platform, the dynamic response characteristics during the drive-to-fly transition were investigated. The results demonstrate the feasibility of the operational principle underpinning the platform. The research provides a feasible technical approach for lightweight and highly integrated aerial-ground platforms, laying the foundation for future experimental implementation.

To enhance the energy and travel efficiency for electric bus routes, a cloud-supported efficient eco-driving control strategy based on a vehicle-cloud collaborative layered architecture is proposed in this paper. At the cloud layer, an efficient velocity planning model balancing energy consumption and travel time is constructed based on route map information and solved using the Dynamic Programming (DP) algorithm. At the vehicle layer, a safety-prioritized arbitration strategy is designed to switch decisions between the cloud-planned optimal velocity and the adaptive cruise control (ACC) car-following velocity to ensure driving safety. An experimental platform based on the vehicle-road-cloud collaborative architecture was constructed, and validation was conducted under real-world intercity road conditions. Experimental results demonstrate that the system exhibits favorable communication, real-time performance, and reliability. Compared with the ACC strategy, the proposed strategy increased average velocity by approximately 4.21% and reduced energy consumption by about 1.12% while ensuring operational punctuality.

Large Language Models (LLMs) are propelling Intelligent Transportation Systems (ITS) toward an agentic paradigm, enabling intent understanding, constraint-aware reasoning, and decision-making. This paper focuses on the Group Meeting Location Recommendation (GMLR) problem, which aims to jointly optimize group preferences and mobility costs under real-world transportation network constraints. However, existing methods often overlook real-world geographic context, fail to jointly coordinate group preferences and spatial feasibility, and rely heavily on static, structured data to model group preferences, limiting their applicability to complex and dynamic group scenarios. We propose MEET-LLM, an agentic, LLM-driven, closed-loop framework for GMLR. MEET-LLM comprises three modules: (1) a Preference Profiling Module that constructs structured semantic representations of users and merchants by using LLMs to extract multilevel preferences from natural language reviews, overcoming the limitations of static aggregation; (2) a Spatial Optimization Module that introduces a mobility cost (MC) metric to evaluate group-level travel convenience and account for user-specific travel constraints; and (3) a Chain-of-Thought (CoT) Reasoning Module that coordinates group preferences and mobility costs through multi-turn LLM reasoning to generate interpretable, executable recommendations with map-based navigation guidance. MEET-LLM jointly coordinates group preferences and spatial feasibility, bridging natural language reasoning with real-world mobility execution. It enables dynamic, structured, and action-ready recommendations in real urban environments, highlighting the potential of LLMs as the cognitive core for agentic mobility coordination in ITS.

Triboelectric nanogenerators (TENGs) are constrained by the inherent material limitations and systemic performance bottlenecks. Conventional polydimethylsiloxane (PDMS) triboelectric layers offer limited control over surface electronegativity, interfacial polarization, and charge retention, hindering stable self-powered operation. To address these challenges, we report a flexible triboelectric system based on a fluorinated metal-organic framework (MOF), UiO-66-4F, which integrates energy harvesting and storage via coordinated design across materials, devices, and circuits. The triboelectric nanogenerator (UF-TENG), fabricated from a UiO-66-4F@PDMS composite layer with 10 wt% filler, generates an open-circuit voltage of 180 V and a short-circuit current of 11 μA, representing 3- and 1.83-fold enhancements over pristine PDMS. The UF-TENG also exhibits stable output over 10,000 cycles under 20% tensile strain. The flexible supercapacitor incorporating 20 wt% UiO-66-4F/LiCl exhibits lower high-frequency impedance and near-ideal low-frequency phase angle, and has typical double-layer capacitance characteristics for efficient charge management. The output-voltage fluctuation of the integrated device is 60% lower than that of a separate TENG, maintaining a stable output for 4,500 s, effectively improving power storage and output stability. This work demonstrates that functional group engineering in MOFs not only enhances triboelectric charge generation but also facilitates efficient charge management, offering a compact, durable, and integrable power solution for wearable electronics.

Developing low-carbon transportation is crucial for ecological conservation and energy security. This paper addresses eco-driving challenges in connected vehicles by proposing a model-guided deep reinforcement learning framework to enhance the practicality and reliability of eco-driving strategies. Integrating model-guided and data-driven approaches, the method first designs a speed guidance algorithm based on the Intelligent Driver Model (IDM), which flexibly adjusts driving conservatism through parameter tuning. Subsequently, a reinforcement learning framework based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed. The reference speed generated by the IDM is embedded into the state space as prior knowledge to guide the agent toward rapid convergence during training. This strategy significantly optimizes vehicle energy consumption while strictly ensuring driving safety and having minimal impact on travel efficiency. Experimental results demonstrate that compared to baseline methods and traditional deep reinforcement learning strategies, the proposed approach achieves superior performance in energy savings, driving smoothness, and traffic continuity. It achieves an average energy savings rate of 7.96% with only a 0.44% increase in travel time, validating its effectiveness and practicality for eco-driving tasks.

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.

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.

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.

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).