The degradation of airport runway surfaces poses persistent safety risks in aviation operations. Runway cracks tend to expand under environmental loads and aircraft cyclic impacts, generating Foreign Object Deposition (FOD) that directly jeopardize aircraft takeoff and landing safety. The International Civil Aviation Organization (ICAO) mandates mandatory inspections for runway surface integrity . However, current inspection methods predominantly rely on manual patrols, which suffer from low efficiency and inadequate early-stage crack detection capabilities—particularly challenging for routine inspections at resource-constrained regional and general aviation airports.This study presents the design and evaluation of an autonomous unmanned ground vehicle (UGV) system for runway crack detection, integrating real-time vision-based deep learning, geospatial localization, and optimal coverage path planning . A YOLO-based model is implemented for onboard crack detection, while a Chinese Postman-based algorithm ensures complete and efficient runway traversal . The system is developed using a Raspberry Pi and STM32 architecture within a ROS framework, and validated through simulation and controlled testing. Results focus on inspection coverage efficiency, localization consistency, detection accuracy, and the practical utility of generated inspection reports . The proposed framework demonstrates the feasibility of a low-cost, integrated solution for automated runway inspection, with potential to reduce manual labor and enable more frequent monitoring.

The choice of driving mode will significantly affect the overall performance of the robot. Therefore, this article mainly focuses on the two mainstream driving technologies: hydraulic drive and motor drive. By analyzing the actual operational data of some typical products, we can understand their performance characteristics, core advantages, and inherent limitations, and then conduct multidimensional comparative analysis. This research result has the potential to provide a theory that can be directly used as a reference for system developers and mechanical designers.

Bayesian update, as a probabilistic inversion method, can dynamically correct model parameters by integrating observation data. However, its effectiveness is affected by various factors, and existing research mainly focuses on the algorithm itself, with insufficient exploration of the impact mechanisms of observation configuration and information quality. This paper addresses the problem of the accuracy of truss structure models being affected by model precision and performance degradation, and studies the application of the Bayesian update method in structural model correction. Systematic numerical simulation experiments were carried out with a specific truss model. The cross-sectional parameters of the elements were set and a Monte Carlo sampling method was used. Using the control variable method, the effects of three key factors on the Bayesian updating effect—information quality, number of measurement points and updating method—were systematically analyzed. The mechanisms through which these key factors affect the performance of Bayesian updating were clarified, providing a theoretical basis and reference for optimizing the placement of measurement points and selecting updating methods in practical engineering.

Efficient electrical energy storage is a core technology supporting the development of portable electronic devices, electric vehicles and other industries. Featuring high power density and long cycle life, supercapacitors fill the performance gap between traditional capacitors and batteries, thus attracting widespread attention. Among all components, electrode materials are the key factors determining the performance of supercapacitors. Zinc cobaltate (ZnCo₂O₄) possesses a stable spinel structure and the bimetallic synergistic effect of zinc and cobalt. It can provide abundant redox active sites and delivers a high theoretical specific capacitance, making it a promising pseudocapacitive material. Nevertheless, pure-phase ZnCo₂O₄ still suffers from insufficient electrical conductivity and structural degradation during cycling. To solve these problems, researchers mainly adopt morphology regulation and composite modification to improve its electrochemical performance. Typical strategies include constructing flower-like microspheres, nanorod arrays and other structures, or combining ZnCo₂O₄ with reduced graphene oxide, metal hydroxides and other materials to optimize electron/ion transport capacity and structural stability. Relevant studies have proved that ZnCo₂O₄-based electrodes exhibit high specific capacitance in the three-electrode system. When assembled into asymmetric supercapacitors, the devices also achieve high energy density and favorable cycling stability. Overall, benefiting from its structural advantages, tunable morphology and great potential for composite modification, ZnCo₂O₄ has broad application prospects in next-generation high-performance supercapacitors. This review systematically summarizes the energy storage mechanism of supercapacitors and the research progress of ZnCo₂O₄-based electrode materials, aiming to provide references for the design of high-performance energy storage devices.

As the installed capacity of photovoltaic (PV) power plants is increasing year by year, an efficient PV inspection system is needed to gain the benefits of PV power generation. Conventional PV inspection is expensive and does not meet the demand for large-scale PV plants. Remote sensing offers wide coverage and high efficiency, meeting various PV fault-detection needs. And it has been primarily applied to PV inspection and has achieved a series of successes. This paper presents optical remote sensing technologies applied to PV power plant inspection, including visible-light cameras, infrared cameras, and light detection and ranging (LiDAR). The conclusions are as follows: visible-light and infrared remote sensing can monitor the surface condition and internal faults of PV modules, and light detection and ranging can detect terrain changes in PV plants. Secondly, novel high-performance sensors ought to be developed to identify a wider range of PV fault categories. Thirdly, multi-sensor fusion is necessary, as a single remote sensor can detect only one fault type and cannot capture all fault types, further reducing the overall efficiency of PV inspection. Finally, the existing optical remote sensing technologies need to be lighter, and the adverse effects of natural illumination need to be mitigated to improve image quality. The paper aims to summarise the applications of each optical remote sensing technology in PV inspection. It also describes the drawbacks of each remote sensing sensor, offering some insights for future work.

Aiming at the problem that -30℃ to 50℃ extreme temperatures restrict the engineering application of power batteries for new energy vehicles, this paper analyzes the electrochemical and thermal performance degradation mechanism of power batteries in harsh extreme environments, and summarizes latest research progress of battery material optimization, integrated thermal management architecture and AI-driven intelligent control strategies. It points out four critical industrial bottlenecks including insufficient system coordination, lack of unified standard test data, imperfect reliability verification system and foreign core patent barriers. The results show that extreme temperatures significantly deteriorate battery performance and safety, and thermal management systems need to effectively balance temperature, energy and power output efficiently. Finally, three key development trends including multi-energy coordination, digital twin simulation verification and low-cost practical engineering are prospected systematically. This study can provide solid theoretical support and practical technical reference for the research and engineering application of power battery thermal management in wide extreme temperature regions worldwide.

Aiming at the keyword model of stainless steel/steel composite plate explosive welding given in simulation data, this paper systematically sorts out the three-dimensional modeling ideas, material parameter system and initiation conditions based on LS-DYNA. Combined with the explosive welding window theory, the rules of interfacial formation are analyzed. According to the analysis of the keyword file, the model consists of an explosive layer, a SUS304 stainless steel flyer plate and a Q345R steel base plate. MAT_HIGH_EXPLOSIVE_BURN and JWL equation of state are adopted to describe detonation loading, while the Johnson-Cook constitutive model and Gruneisen equation of state are used to characterize the high-velocity impact response of metals. The typical structural dimensions of the model are as follows: the flyer plate is 400 mm×300 mm×6 mm, the base plate is 360 mm×260 mm×26 mm, the explosive layer is 20 mm thick, and the initial stand-off distance is 14 mm. Two groups of initiation point settings are further compared: one is set near the left edge of the explosive layer, and the other is arranged at the partial central area of the explosive surface. The analysis results show that edge initiation is more conducive to forming a collision front that advances steadily along the welding direction, and it can generate a continuous plastic deformation zone on the flyer plate as well as a more uniform interfacial evolution path. By contrast, off-center initiation tends to introduce local curvature effect and inhomogeneous loading on the plate surface. The results obtained in this paper provide reusable model basis for subsequent supplementary calculation, extraction of post-processing results such as interfacial pressure, collision velocity and equivalent plastic strain, and improvement of result discussion in student conference papers.

This paper proposes an AI-based intelligent house-moving system integrating digital twin technology with AI-driven planning algorithms and virtual–physical interaction technology to improve relocation process efficiency. Structure-from-Motion (SfM) approach combined with object detection techniques is applied to the system which generates semantically enriched digital twin models for indoor environment and objective objects. Based on this model, feasibility analysis and path planning are performed to evaluate whether large furniture can be moved through constrained spaces and hence generates collision-free movement paths for different scenarios under each case. Genetic algorithms are used for optimizing container space utilization, improving packing efficiency and reducing transportation cost, meanwhile, reinforcement learning is incorporated to enhance system adaptability. In addition, with the help of augmented reality (AR) module and digital twin visualization dashboard, the system can provide real-time guidance and monitoring for users. The proposed system improves efficiency, accuracy, and user experience compared to traditional relocation methods.

In practical power systems, transformers are continuously involved in voltage conversion and electrical-energy transmission, so once abnormal operating conditions appear inside the equipment, the influence may gradually spread to the stability of the entire grid. Because early-stage transformer faults are usually weak and not easy to observe directly from conventional electrical quantities, this paper investigates a fault-monitoring method based on current-signal trajectories and discusses its application to transformer operating-state analysis. In the proposed approach, the primary-side and secondary-side current signals are taken as the basic data source, and the corresponding Lissajous trajectories are then constructed under different operating conditions. Instead of analyzing only waveform amplitude or phase separately, the method converts the current relationship into geometric trajectory characteristics, making the variation process easier to observe visually. To examine whether the method remains effective under different fault levels, a transformer equivalent-circuit model was built in Multisim, and several abnormal operating conditions were reproduced by changing equivalent impedance parameters and related electrical quantities. During the simulation analysis, several ellipse-related quantities were extracted from the generated trajectories, mainly including ellipse area together with the ratio between the major axis and minor axis. These geometric quantities were then compared under healthy and faulty operating conditions. The obtained results show that even under relatively weak parameter disturbances, such as ±3% variations, the trajectory shape had already changed slightly compared with the normal state, and the corresponding feature deviation approached about 5% in some simulation cases. After the disturbance level continued to increase, especially when the parameter variation reached around ±30%, the deformation of the ellipse became much more obvious, while part of the geometric feature variation exceeded 32%. From the overall simulation results, it can be seen that the trajectory characteristics still maintain visible differences under different transformer operating conditions, including relatively weak abnormal states appearing at an early stage. Compared with directly using conventional electrical signal quantities for analysis, the proposed method represents operating-state variation through geometric trajectories, so some small parameter changes can be reflected more intuitively. The study therefore suggests that current-signal trajectory analysis may provide another possible way for transformer online monitoring and operating-condition evaluation in practical engineering applications.

Photosynthetic organisms in nature achieve directional transmission and conversion of solar energy via sophisticated light-harvesting complexes, a process closely associated with Förster resonance energy transfer (FRET). When focusing solely on individual photons capable of exciting chlorophyll molecules, the maximum energy transfer efficiency can reach approximately 95%. Inspired by this, numerous artificial-light-harvesting systems (ALHSs) based on cascaded FRET have been developed. Nevertheless, only a handful of examples realize an overall energy transfer efficiency exceeding 80%. Among them, ALHSs with near-infrared phosphorescent emission exhibit distinctive properties, including low toxicity, large Stokes shifts and tunable phosphorescence, and thus have been widely applied in information encryption, bioimaging, materials science and other fields. Fundamentally, the development of near-infrared emissive ALHSs requires lowering the energy level of the molecular triplet excited state (T1). But this change makes non-radiative transitions more likely, so the brightness and duration of phosphorescence both go down. Besides, the energy difference between S1 and T1 becomes much bigger. This slows down intersystem crossing, which also weakens phosphorescence. We thus need new ways to get efficient energy transfer and near-infrared phosphorescence from organic dyes.