[Objective] Environmental perception is a core task in autonomous driving, and high-precision object detection is essential for ensuring the safety and stability of autonomous driving systems. In recent years, LiDAR has been widely adopted in autonomous driving as a key sensor for three-dimensional perception due to its advantages, such as immunity to lighting conditions and high ranging accuracy. [Methods] This study first reviewed traditional camera-based object detection methods and analyzed their limitations in complex environments, then introduced the development history, working principles, types, and key parameters of LiDAR, followed by a systematic review of object detection methods based on point cloud representations, voxel representations, and multi-sensor fusion strategies. The network architectures, advantages, and challenges of different methods were compared and analyzed, and quantitative performance evaluations were conducted based on experimental results from the KITTI detection dataset. In addition, this study introduced a perception framework based on the bird's eye view (BEV) perspective and the trend of multi-sensor fusion, and analyzed the trade-offs between detection accuracy, real-time performance, and environmental adaptability of the current algorithms. [Results] The advantages of LiDAR-based object detection were summarized, and key future research directions were proposed to address challenges such as point cloud sparsity, high computational overhead, and the complexity of multimodal fusion. [Conclusions] Continuous optimization of algorithms and hardware enhances the accuracy, robustness, and practicality of LiDAR object detection in complex scenes.

[Objective] Overhead high-voltage transmission lines, as an important component of the power system, are exposed to the natural environment for a long time. Their metal components are prone to rust, which seriously affects the safe and stable operation of the power grid. The traditional manual inspection method is limited by the danger and complexity of high-altitude operations, making it difficult to achieve comprehensive and accurate defect detection. Although the existing inspection methods based on unmanned aerial vehicles (UAVs) have improved in efficiency, they still have deficiencies in aspects such as the acquisition of three-dimensional spatial information, the accuracy of path planning, and the accuracy of rust identification. An autonomous flight inspection method based on UAVs is expected to be developed combining laser three-dimensional (3D) modeling and support vector machine (SVM), so as to enhance the accuracy and efficiency of rust detection for transmission lines and provide reliable technical support for the intelligent operation and maintenance. [Methods] The multi-technology integration strategy was adopted to achieve the precise detection of rust defects in transmission lines. A laser 3D scanner was used to scan the transmission lines and their surrounding environment. Based on the kernel density evaluation function, the point cloud data were processed to establish a high-precision 3D model. In terms of prone autonomous flight, the altitude ratio parameter was introduced to identify obstacles. Combined with image processing and sonar feedback, constant altitude flight was achieved. Based on bounding box analysis, the heading angle was dynamically adjusted to ensure the safety and efficiency of the flight path. In the rust defect identification, the SVM algorithm was adopted to extract features and classify the pre-processed images. By normalizing the input data and optimizing the classification hyperplane, the accuracy of rust detection was improved. The experiment employed a high-resolution camera (4 096 pixels×3 072 pixels) to collect images of transmission lines. A total of 1 201 sample images were obtained and divided into the training set and the test set at a ratio of 7∶3 to verify the effectiveness of the method. [Results] The experimental results show that the proposed method demonstrates significant advantages in path planning and defect identification. The UAVs can precisely avoid randomly distributed obstacles and plan the optimal inspection path, superior to the traditional methods in both safety and efficiency. In terms of rust defect identification, the identification rate of the SVM-based model for 360 test images stably remains above 95％, which is significantly superior to the reinforcement learning method and the deep residual network method. In the rust defect detection of different areas, this method exhibits good adaptability and stability. Through the frames per second (FPS) evaluation, the real-time detection performance of this method is excellent, which meets the needs of large-scale transmission line inspections. According to the visualization results, this method can accurately mark the rusted area and effectively avoid false detection and missed detection. [Conclusions] The proposed autonomous flight inspection method of UAVs for rust defects in transmission lines, integrated with laser 3D modeling, intelligent path planning, and the optimized SVM model, excels at the accuracy of path planning, defect identification rate, and real-time capability. The experiment verifies the engineering application value of this method in increasing efficiency, accuracy, and safety of inspection. Future research can further optimize the adaptability of the algorithm in complex environments and expand its application in defect detection of other power equipment.

[Objective] In the context of the global vigorous promotion of clean energy transformation, photovoltaic power generation, as a green and sustainable energy source, is becoming increasingly important in the energy structure. However, photovoltaic panels are exposed to the outdoors for a long time and are affected by natural factors such as strong winds, sand, rainfall, and bird activities. These factors often lead to problems such as panel damage and stains, which seriously reduce the photovoltaic power generation efficiency and system stability. The traditional manual operation and maintenance method not only consumes a large amount of human, material, and time resources but also has difficulty in ensuring the timeliness and effectiveness of operation and maintenance in complex terrains and harsh weather conditions. Therefore, it is urgent to develop highly efficient and intelligent photovoltaic panel operation and maintenance technologies. [Methods] An innovative photovoltaic panel operation and maintenance solution was proposed in this paper. It integrated a high-efficiency object recognition network based on Mamba module and a path-planning strategy using an improved particle swarm optimization algorithm to promote the intelligent operation of photovoltaic operation and maintenance robot. In the object recognition stage, the Mamba module was applied to construct an object detection network. The unique architecture of Mamba enabled it to accurately capture the subtle damage textures and stain marks on photovoltaic panels and quickly identify abnormal panels. The multi-scale detection strategy was introduced to extract and fuse image features at different scales, effectively solving the problems of easy loss of small-object features and information loss caused by occlusion between panels. It significantly improved the detection accuracy and speed, meeting the real-time requirements of photovoltaic operation and maintenance. In terms of path planning, the traditional particle swarm optimization algorithm was optimized and improved, and an adaptive inertia weight update strategy was introduced. This strategy adjusted the particle search behavior in real time dynamically according to the detection and positioning results of the object recognition network, enabling the particles to quickly converge to the global optimal solution. It planned the shortest and most effective maintenance path for the operation and maintenance robot, avoiding invalid and repeated paths and greatly improving the operation and maintenance efficiency. [Results] The results of simulation experiments and practical project tests show that this method has achieved remarkable results in terms of detection accuracy and path planning. In terms of detection accuracy, the average detection accuracy for various types of damaged and stained panels is significantly higher than that of traditional detection algorithms. Regarding the path planning effect, compared with traditional algorithms, the proposed method greatly enhances the working efficiency of photovoltaic operation and maintenance robots, providing reliable technical support and practical examples for the intelligent and efficient operation and maintenance of photovoltaic panels. [Conclusions] The proposed method performs outstandingly in terms of detection accuracy and speed. It effectively improves the working efficiency of photovoltaic operation and maintenance robots, provides a practical and innovative solution for the operation and maintenance of photovoltaic panels, and thus has high application value and broad promotion prospects.

[Objective] The internal environment of intelligent substations is complex, with narrow channels varying in shapes and sizes. The complexity and uncertainty of these narrow channels require robots to frequently adjust their poses when they are passing through, thus increasing the path planning difficulty. Therefore, a narrow channel path planning method for quadruped inspection robots in intelligent substations was proposed. [Methods] The sensors inside the quadruped inspection robot were employed to estimate its own state, and external sensors were combined to achieve perception and positioning of the surrounding environment. By adopting the probabilistic positioning principle model, the pose estimation value of the robot in the intelligent substation was obtained by combining the observation results of LiDAR and mileage prediction results. Based on the estimated robot pose results, the problem of establishing a map was transformed into a maximum likelihood estimation problem of the map. Additionally, SLAM technology was utilized to process data, estimate the robot pose, construct a grid map, and update grid states. After completing the grip map establishment, the probability roadmap algorithm was adopted to plan the inspection path. The Gaussian sampler was employed as a roadmap sampling tool to randomly select a point in the grid map and collect a point along a random direction at a distance from the point. If the collection point is located in a blank grid, it is considered a collected roadmap point. Meanwhile, a Gaussian sampler was leveraged to collect a large number of sample points distributed around obstacles, with the number and contours of obstacles in the environment determined via the learning process. In this study, the starting and ending points were determined, and the number of sampling point nodes and the set of probabilistic roadmaps were set. The probabilistic roadmap set was initialized according to the starting and ending points, with new sampling points generated by sampling the grid map space. According to the probabilistic roadmap algorithm, a set of planned inspection path points were obtained, and the starting and ending points were set, with a set of path optimization points established. By employing the starting point of the path as the test point, the set of roadmap points on the path were tested one by one. If the test point cannot be connected to a certain point, it is considered a turning point, and is stored in the optimized point set and employed as a new test point. The roadmap points were tested backward one by one until reaching the ending point. After completing all tests, the roadmap points in the optimization point set were connected one by one to form a complete inspection planning path of quadruped inspection robots, thereby reducing the robot's pose direction conversion during inspection and ensuring the completion of inspection path planning without colliding with obstacles. [Results] The experimental results show that when this method is adopted for narrow channel path planning in substations, the number of sampling nodes is less than 52, and the average path cost is below 87 m. [Conclusions] The proposed method is validated to have sound planning effectiveness and superior performance.

[Objective] Against the backdrop of the global energy transition and the “dual carbon” targets, energy demand in cold regions has risen sharply due to natural conditions such as low temperatures, snowfall, and permafrost, which adversely affect the efficiency, service life, and operational safety of energy storage systems. To develop new energy storage technologies suitable for cold regions, it is urgently needed to establish a comprehensive performance evaluation system covering the entire service lifecycle to support industrialization and green energy utilization. This study aimed to establish an evaluation framework and conduct a technical comparison to promote the rational deployment of different energy storage pathways, thus addressing regional energy security and sustainable development requirements. [Methods] This study focused on four major categories of energy storage technologies:electrochemical, mechanical, thermal, and hydrogen-based energy storage. It systematically analyzed five evaluation indicators:economic performance, technical performance, safety and reliability, environmental friendliness, and lifecycle energy efficiency. In terms of economic performance, total capital cost and levelized cost analysis were employed, with a low-temperature correction factor introduced. In terms of technical performance, low-temperature adaptability, energy efficiency, and cycle life were analyzed. In terms of safety and reliability, a multi-dimensional safety grading system was constructed using fault diagnosis methods. In terms of environmental friendliness, the entire chain from raw material acquisition to manufacturing and power plant construction was considered. Lifecycle energy efficiency was measured using the energy return on investment (EROI) and energy storage on investment (ESOI) indicators. Through horizontal comparison and vertical analysis, this study revealed the differential impacts of cold environments on the performance and cost of various energy storage technologies. [Results] The results show that lithium-ion batteries have reduced lifespan and safety under low temperatures. Flow batteries have long lifespans and high cycling stability, but electrochemical energy storage carries the risk of thermal runaway. Compressed air energy storage offers significant advantages in long-term energy supply and cost controllability, and its lifecycle energy efficiency is higher than that of electrochemical energy storage, but is constrained by geological conditions. Flywheel energy storage has a fast response speed but high manufacturing and maintenance costs at low temperatures. Sensible heat storage and chemical heat storage in thermal energy storage systems show potential for seasonal regulation in cold regions. Hydrogen energy storage, with its high specific energy and multi-energy coupling characteristics, demonstrates unique value in inter-seasonal peak regulation and microgrid applications, but faces challenges such as high-pressure leakage and hydrogen embrittlement. [Conclusions] Future efforts should intensify low-temperature adaptation measures and strengthen subsidies for temperature control, promote the development of multi-energy complementary energy storage dispatch centers, enhance overall system resilience, and establish a standard system for energy storage in cold regions covering the entire construction, operation, and end-of-life recycling, so as to achieve a balance between economic efficiency and safety and promote the healthy development of the energy storage industry in cold regions.

[Objective] In power systems, load data analysis is crucial for power grid dispatching, planning, and management. However, with the deepening of the complexity and intelligence of power systems, power load data exhibit characteristics of high dimensionality and sparsity, which poses significant challenges to traditional data analysis methods in terms of processing efficiency and ability to capture the intrinsic information of load changes. An efficient unsupervised data mining algorithm was proposed in this paper, which was aimed at improving the processing efficiency and information extraction capability of high-dimensional sparse power load data. [Methods] Firstly, a feature ranking method based on information entropy was adopted to determine feature importance. Data initialization was completed by calculating mutual information and conducting centralization and standardization. Features with the maximum mutual information were selected to expand the feature set, and feature subsets were screened by calculating relevant information entropy. The subset screening process was optimized using support vector machine (SVM)classifier as the benchmark model, and an improved particle swarm optimization algorithm was introduced for secondary feature selection. Meanwhile, the SVM classifier was used to complete the preliminary feature screening. Secondly, the principal component analysis (PCA) was introduced for dimensionality reduction. The sample matrix was centralized, and the covariance matrix was established. Eigenvalues and eigenvectors were obtained, and eigenvectors were selected to construct a new matrix to achieve dimensionality reduction. Finally, an autoencoder network based on unsupervised learning was introduced to conduct unsupervised mining. In the encoding stage, input data were converted into feature representations. In the decoding stage, data recovery was completed. Through steps such as data setting, clustering execution, data point screening, data balancing processing, and model training to obtain a classification interface, hidden feature extraction and network adjustment were realized. [Results] When the algorithm in this paper is applied, the Rand index values all exceed 0.60, indicating high clustering accuracy. In 60 iterations of experiments, the maximum memory overhead ratio is about 8.3％, demonstrating the algorithm's high efficiency in computing resource utilization. Compared with other traditional methods, this algorithm can achieve higher processing efficiency and better mining results when dealing with high-dimensional sparse power load data. [Conclusions] The unsupervised mining algorithm performs excellently in the analysis of high-dimensional sparse power load data. By reducing computational complexity through feature selection and dimensionality reduction, and mining nonlinear features with the autoencoder network, it significantly improves the accuracy and efficiency of data mining, and has strong applicability and feasibility. Its innovation lies in integrating multiple methods such as information entropy-based feature ranking, SVM, improved particle swarm optimization, PCA, and autoencoder network to form a complete system from feature processing to data mining. This system can not only effectively address the challenges in mining high-dimensional sparse power load data but also provide a new and effective means for load data analysis in power systems, which is of great significance for promoting the intelligent development of power systems.

[Objective] Dry-type air-core reactors (DARs) are prone to inter-turn short circuits during operation. Characterized by low detectability and limited real-time observability, such faults pose a serious threat to the safe and stable operation of power grids. Although widely adopted in engineering, detection methods based on magnetic flux density amplitude exhibit limitations. Specifically, they are insensitive to inter-turn short circuits in inner windings, and detection accuracy is significantly degraded by magnetic field interference from adjacent equipment when multiple DARs operate in parallel. To enhance fault identification capability and achieve early warning, a DAR inter-turn short circuit detection method based on the magnetic dield direction angle (MFDA) was proposed. [Methods] Based on finite element simulation technology, the surrounding magnetic field distribution of a single-phase DAR under normal operation and inter-turn short circuits at different locations was analyzed. By arranging multiple monitoring points around the DAR, magnetic flux density data were collected, and the variation patterns before and after the fault were analyzed. Based on the principle of magnetic field superposition, the temporal variation patterns of MFDA at each monitoring point near the faulty phase in a three-phase DAR were calculated and analyzed. By comparing the MFDA data of a single DAR with those of multiple parallel DARs, the interference level of adjacent normally operating DARs on fault detection was quantitatively evaluated, and the placement of monitoring points was optimized. [Results] Simulation results indicate that after an inter-turn short circuit occurs in a single-phase DAR, both the amplitude and direction of the surrounding magnetic flux density undergo significant changes. Compared with the amplitude-based detection method, MFDA exhibits higher response sensitivity to inter-turn short circuits and can accurately capture the changes in electromagnetic characteristics caused by faults. By analyzing the temporal variation of MFDA at each monitoring point before and after the fault and establishing a fault decision threshold, inter-turn short circuits in single-phase DARs can be diagnosed. For the scenario of three-phase DARs operating in parallel, arranging monitoring points near the faulty phase and on the symmetric perpendicular bisectors of the normal phases can effectively mitigate magnetic field interference from adjacent DARs. By combining the temporal characteristics of MFDA with the fault decision threshold, short-circuit faults can still be diagnosed. [Conclusions] The proposed MFDA-based detection method can effectively address the limitations of the magnetic flux density amplitude detection method in terms of sensitivity and anti-interference capability, providing a reliable technical approach for real-time monitoring of DAR operating status and fault early warning. Based on the simulation results, the proposed method shows strong potential for engineering applications and provides practical support for improving the operation and maintenance of power grid equipment and ensuring the safe and stable operation of power systems.

[Objective] As the digital transformation of substations accelerates, traditional evaluation methods have limitations in the accuracy and adaptability of reliability analysis, and the reliability of substations is of great significance for the stable operation of the power system. To this end, a reliability analysis method based on the improved dynamic Bayesian network (DBN) for a 110 kV digital substation model was proposed to enable real-time monitoring and accurate evaluation of the system's status. [Methods] Firstly, statistical analysis of the key parameters was conducted, such as failure rates of various types of equipment and components within the substation, thus constructing the basic data for reliability evaluation. Secondly, DBN was introduced as the modeling tool. The network structure was dynamically adjusted and redesigned in response to environmental factors such as temperature, humidity, and load fluctuations to enhance the model's adaptability to a non-stationary operating environment. Finally, fault tree analysis (FTA) was employed to identify logical relationships of system-level faults, and the results were systematically mapped into DBN to build a probabilistic reasoning model with both hierarchical and causal characteristics. By adopting probabilistic reasoning to compensate for information gaps, reasoning robustness and accuracy can be improved by this method under incomplete information or missing data. [Results] Experiments conducted on the 110 kV digital substation model show that the area under the ROC curve of the proposed method is the closest to 1, indicating that the analysis results are the closest to the actual values. Meanwhile, it exhibits the lowest error rates and stronger stability in the reliability analysis of the three substations, with the accuracy, precision, recall, and F₁ scores being 0.891, 0.875, 0.904, and 0.889, respectively. Thus, its overall performance is better than the comparative methods. [Conclusions] The proposed method exhibits significant advantages in terms of accuracy, stability, and adaptability. By integrating the structured modeling capabilities of FTA with the adaptive reasoning mechanism of DBN, it effectively overcomes the limitation of insufficient evaluation accuracy of traditional methods in dynamic environments and information deficiency conditions. This method not only achieves dynamic quantification of reliability indexes for substation digital models but also provides a reliable theoretical support and practical tool for system status monitoring and intelligent operation and maintenance, with promising engineering application prospects.

[Objective] In wireless communication systems, signals are not only affected by Gaussian noise, but also interfered by impulse noise, such as electromagnetic interference and lightning noise. This noise is featured with non-Gaussian, abruptness, and strong impulse, which leads to severely degraded performance of traditional signal processing methods based on Gaussian noise. Binary phase shift keying (BPSK), as a traditional basic modulation method, is widely used in satellite communication, underwater acoustic communication, military communication and other fields. Its symbol rate estimation is a key link in signal demodulation, synchronization and parameter blind recognition. However, in the impulse noise environment, traditional symbol rate estimation methods rely on the statistical characteristics of the signal, resulting in severe deterioration or even complete failure of estimation performance. The cyclic spectrum theory can effectively utilize the cyclostationary characteristics of signals to separate signals and noise in the frequency domain and the cyclic frequency two-dimensional plane, providing ideas for parameter estimation in impulse noise environments. However, traditional cyclic spectrum theory methods are susceptible to impulse noise interference during the extraction of cyclic spectral lines, resulting in reduced estimation performance. [Methods] To address this issue, a high-precision symbol rate estimation method was proposed based on an improved cyclic spectrum. This method employed the cyclostationary characteristics of BPSK signals to construct a corresponding nonlinear function, and sequentially calculated the autocorrelation function, cyclic correlation function, and cyclic spectrum function. The impulse noise and white Gaussian noise (WGN) were both zero at non-zero cyclic frequencies in the BPSK signal cyclic spectrum after nonlinear transformation. With these characteristics, the symbol rate of the signal was accurately estimated by searching for spectral peaks and corresponding cyclic frequencies on the Fourier frequency and spectral line plane of the received signal cyclic spectrum. [Results] Nonlinear transformation optimizes the second-order statistics of the signal while keeping the phase information unchanged, thereby reducing the interference of impulse noise. This method shows superior performance in impulse noise environments. The suppression mechanism of the improved cyclic spectrum on α stable distribution is revealed and the effectiveness of discrete spectrum peak detection is verified, providing a highly robust symbol rate estimation scheme for impulse noise environments. [Conclusions] Compared with the wavelet transform method, the proposed method can effectively lower the effect of impulse noise on symbol rate estimation, with higher estimation accuracy and lower algorithm complexity. According to computer simulation results, the proposed method has a normalized mean square error (NMSE) lower than that of the existing methods. As the value of stable distributed α increases, the NMSE decreases. This further demonstrates that the proposed method has higher estimation accuracy, more significant performance advantages at high signal-to-noise ratios (SNR), and better robustness under different impulse noise conditions. The application scenario is broader.

[Objective] The magnetic circuit method is a rapid analytical technique employed for analyzing pipeline magnetic fields and optimizing the design and performance of detection devices. Existing traditional magnetic circuit models based on idealized assumptions often neglect the effects of fringing flux and the nonlinear characteristics of ferromagnetic materials when calculating the internal magnetic field in pipelines. This leads to significant discrepancies between theoretical models and measured results, directly affecting the reliability and accuracy of detection device design. [Methods] To address these issues, this study proposed a magnetic circuit modeling method for pipeline axial magnetization based on magnetic field segmentation, which focused on the practical impact of fringing reluctance to establish a more complete mathematical model of the magnetic circuit. The magnetizing device and the pipeline were treated as a coupled system. According to the distribution path of the fringing magnetic field in the excitation zone, the magnetic field segmentation method was employed to divide the system into cylindrical and spherical subregions. The magnetic permeance expressions for each subregion were derived separately, thus constructing a more accurate mathematical description of the air-gap permeance. Meanwhile, the expression of the pipeline magnetization characteristics was obtained by analyzing the magnetization characteristics of X52 carbon steel. On this basis, an equivalent magnetic reluctance network was introduced to establish an axial magnetic circuit model for pipelines that accounts for fringing effects. The magnetic field intensity in the air gap of the magnetized zone and the distribution of the effective internal magnetic field in the pipeline were calculated. To validate the accuracy of the model, a three-dimensional finite element simulation model of permanent magnet magnetization for pipelines was constructed, and an experimental platform was set up to physically measure the air-gap magnetic field at the excitation end. Finally, based on verification of the effectiveness of the proposed model, a systematic analysis was conducted on the influence mechanism of the structural parameters of permanent magnet magnetization detection devices on the effective magnetic flux inside the pipeline. [Results] Compared with traditional magnetic circuit models, the proposed magnetic circuit model demonstrates higher accuracy in calculating the internal magnetic field of pipelines. The calculation results are in good agreement with finite element simulation results, with the relative error of magnetic field intensity not exceeding 5%. In the experimental verification of the air-gap magnetic field, the error between the calculated values of the improved model and the measured values is less than 30%. This error mainly originates from sensor positioning deviations and material property fluctuations during actual measurements, while the model itself still exhibits strong robustness. Further analysis indicates that the fringing effects of the magnetizing device cause significant attenuation of the effective magnetic flux inside the pipeline, and the leakage flux coefficient is proportional to the effective magnetic path length of the magnetic poles. When the pole spacing is 1.25 times the effective magnetic path length of the poles, the pipeline can be magnetized to saturation while the fringing magnetic field is effectively suppressed. [Conclusions] The proposed axial magnetic circuit model for pipelines considering fringing effects not only deepens the understanding of the internal magnetic field distribution, but also provides important theoretical support and modeling guidance for the structural design of high-precision permanent magnet detection devices. This work is of positive significance for improving the technical level of pipeline nondestructive testing.

[Objective] In the surface of metal materials, the hot dipping method is adopted to produce pure zinc (Zn) layers, which is an efficient method of preparing corrosion-resistant protective layers. However, during the production of hot dipping Zn, since liquid Zn has a strong diffusion ability and reactivity, it will react with the iron elements in the immersed rolls and other components, thus generating iron and Zn alloy brittle intermetallic compounds. This will continue to consume the iron elements in the components, and the corroded iron slag attached to the immersed rolls will also scratch the cladding layer. [Methods] The plasma cladding method was adopted to prepare two cobalt-based alloy cladding layers with the thickness of about 5 mm on the 316L stainless steel substrate, and the two alloy cladding layers were placed in molten liquid Zn at 450 ℃ for 72 h, 120 h, and 168 h to analyze the morphologies, products and element loss after corrosion and investigate the corrosion behavior and mechanism of cobalt-based alloys. [Results] γ-Co and carbides Cr₇C₃ and Cr₂₃C₆ exist in the original organization of the two alloy cladding layers, but the number of carbides in 2^＃ alloy cladding layer is higher with a more diffuse distribution. The corrosion mode of the 1^＃ alloy cladding layer is homogeneous dissolution corrosion, and a diffusion layer with more pores and transverse cracks appears during corrosion. Due to the reaction of Zn with C, element C diffuses from inside to outside, leading to increased C content on the surface of the diffusion layer and cladding layer. After 168 h of corrosion, the action of thermal stress leads to the extension of transverse cracks, and the diffusion layer is finally peeled off into the upper and lower layers, with the intrusion of Zn between the layers. During the corrosion of the 2^＃ alloy cladding layer, the diffusion layer also appears, but there are few pores and cracks, with no occurrence of diffusion layer splitting. During the corrosion process, Zn first diffuses to the grain boundary between carbides and Co solid solution, and then dissolves and corrodes the Co solid solution and gradually bypasses the carbide phase, resulting in gradual thinning of the cladding layer thickness. The corrosion resistance of both alloy cladding layers is higher than that of the 316L stainless steel substrate, and the corrosion rates of the 1^＃ alloy cladding layer and 2^＃ alloy cladding layer are about 1/3 and 1/4 of that of the 316L stainless steel substrate respectively. Although the carbide types in the microstructure of the two alloy cladding layers are almost the same and the carbides do not react with Zn, the carbide content of the 2^＃ alloy cladding layer is relatively higher. Carbides can stop the diffusion of Zn, leading to a decrease in the base solid solution that can react with Zn. Therefore, the 2^＃ alloy cladding layer with higher carbide content has better corrosion resistance. [Conclusions] The carbides generated in situ during plasma cladding have a better protective effect on the cobalt-based alloys, and their corrosion rate decreases dramatically with the increasing content of carbide-forming elements. The corrosion results for different time show that the increase in carbide content can effectively improve the service time of the immersed rolls in liquid Zn.

[Objective] The research on rare earth permanent magnet materials can be traced back to the 1 960 s at the earliest. At that time, after studying the alloys composed of rare earth elements and metals such as iron and cobalt, it was found that these alloys had strong magnetic properties. However, the properties of magnetic materials obtained at the initial stage are unstable, thus limiting their practical application. Due to their excellent magnetic properties, NdFeB magnets are applied to nuclear magnetic resonance instruments in the medical field, generators in the energy industry, and traction motors of new energy vehicles. However, their application is limited by the low coercive force and poor corrosion resistance of magnets in harsh high-temperature and high-humidity environments. [Methods] Advanced nanotechnology was adopted to prepare alloy nanosized powders to improve the magnetic properties and corrosion resistance of sintered NdFeB (S-NdFeB) magnets. First, Cu-Sn alloy nanosized powder prepared by the direct current (DC) arc method was uniformly mixed with NdFeB micro-powder developed by rapid cooling and air flow grinding. Then, the initial blanks were obtained via vacuum sintering at 1 080 ℃ and secondary tempering at 960 ℃ and 450 ℃. Additionally, the blanks were processed by wire cutting and polishing. Finally, property tests and morphology characterizations were conducted on S-NdFeB magnets with different contents of Cu-Sn alloy nanosized powders. [Results] The results show that the Cu-Sn alloy nanosized powder prepared by the DC arc method has quasi-spherical morphology, and its average particle size is about 40 nm. Cu-Sn alloy nanosized powders can effectively alter the potential at the grain boundaries of S-NdFeB, lower the liquid-phase sintering temperature, and penetrate the main phase surface layer from the grain boundaries to improve corrosion resistance, magnetic density, and intrinsic coercive force. In S-NdFeB micron-sized magnetic powders, the uniformity of the main phase in the magnet and wettability of the grain boundaries can be improved by adding an appropriate amount of Cu-Sn alloy nanosized powders. Under the addition of Cu-Sn alloy nanosized powders with a mass fraction of 0.4%, the intrinsic coercive force of the S-NdFeB magnet can reach 1 031 kA/m, and the residual magnetism is 1.28 T, which increase by 9.8％ and 4.1％ respectively compared with the situation without the addition of nanosized powders. After adding the Cu-Sn alloy nanosized powders with a mass fraction of 0.2%, the corrosion potential of the S-NdFeB magnet is increased from -0.851 V to -0.728 V, the corrosion current density is decreased from 53.12 μA/cm² to 42.19 μA/cm², and the change amounts are 14.5％ and -20.6％ respectively compared with the situation without the addition of alloy nanosized powders. Therefore, a significant improvement in the corrosion resistance of the S-NdFeB magnet can be achieved by the appropriate addition of Cu-Sn alloy nanosized powders. [Conclusions] The Cu-Sn alloy nanosized powders prepared by the DC arc method can improve the grain boundary potential and liquid-phase sintering temperature of S-NdFeB magnets after mixed vacuum sintering, and change the surface layer structure of the main phase via grain boundary diffusion. Under the premise of keeping the magnetic properties unchanged, Cu-Sn alloy nanosized powders can improve corrosion resistance of NdFeB magnets and intrinsic coercive force, conserve heavy rare earth resources, and reduce costs. Thus, it is of great significance to improve the magnetic properties and corrosion resistance of magnets by employing Cu-Sn alloy nanosized powders.

[Objective] As transportation infrastructure construction rapidly advances, large-section tunnels are increasingly applied in highways, railways, and other projects. As a commonly adopted tunnel excavation method, the drilling and blasting method can achieve efficient rock breaking. However, under the action of blasting load, cumulative damage will be caused to the surrounding rock of the tunnel, thereby affecting the surrounding rock stability and tunnel construction safety. Therefore, it is of engineering significance to study the cumulative damage characteristics of the surrounding rock of large-section tunnels under blasting load. [Methods] Based on the actual tunnel engineering projects, the technical route of combining numerical simulations and field tests was adopted to systematically explore the evolution rules of cumulative damage of large-section tunnel surrounding rock under blasting load. In terms of numerical simulation, a tunnel model consistent with the actual engineering situation was built with the help of ANSYS/LS-DYNA to simulate the blasting process of multi-section blast holes of a single section, with the focus on analyzing the damage state changes of the surrounding rock under different blasting loading times. On-site blasting monitoring tests were carried out to verify the accuracy of the numerical simulation results. Monitoring points were arranged at key parts of the surrounding rock, and high-precision monitoring equipment was employed to collect data such as particle vibration velocity during blasting in real time. [Results] Numerical simulation results show that the damage range and damage degree of the surrounding rock nonlinearly increase with the blasting load times, and the influence of different detonator sections on the damage of the surrounding rock varies. On-site monitoring results indicate that there is a negative correlation between the peak particle velocity at the measuring point and the distance from the blasting center. By comparing the measured fitted vibration velocity with the numerical simulation results, it is found that the general trend of the two is consistent, and the deviation is small, which effectively verifies the accuracy of the numerical model. [Conclusions] The research results can provide a theoretical basis for the blasting construction of large-section tunnels, which is helpful for optimizing the blasting design, reasonably controlling the blasting parameters, and effectively reducing the cumulative damage of blasting to the surrounding rock. Additionally, tunnel construction safety and surrounding rock stability can be ensured, with references provided for the scientific construction of similar projects.

[Objective] By investigating the influence of modified materials under the combined action of basalt fiber (BF) and iron tailings sand (ITS) on the properties of recycled concrete, this study aims to enhance the recycling rates of construction waste and ITS, and thus promote the development of green building materials. Given the growing scarcity of traditional natural aggregate resources, it explored effective approaches to preparing high-performance recycled concrete by employing recycled aggregate and industrial waste. [Methods] An orthogonal experimental design was adopted to systematically analyze the effects of five factors on the mechanical properties and water absorption of recycled concrete, including the BF volume fraction, ITS substitution rate, substitution rate of recycled coarse aggregate, recycled brick-concrete aggregate mix ratio, and water-binder ratio. Meanwhile, concrete specimens were prepared by utilizing the cement-coated sand and gravel mixing technique, and the compressive strength, flexural strength, splitting tensile strength, and water absorption of the specimens with a curing age of 28 days were tested. [Results] The experimental results indicate that the BF volume fraction, substitution rate of recycled coarse aggregate, and water-binder ratio are the primary factors influencing the mechanical properties of recycled concrete. The comprehensive mechanical properties are optimal under the BF volume fraction of 0.1％, ITS substitution rate of 30％, recycled coarse aggregate substitution rate of 50％, recycled brick-concrete aggregate mix ratio of 1∶2, and water-binder ratio of 0.35. Specifically, the compressive strength reaches 53.6 MPa, with flexural strength of 7.81 MPa, splitting tensile strength of 4.7 MPa, and water absorption of 2.8％. Furthermore, compared to the concrete prepared with natural aggregate, the concrete prepared with recycled coarse aggregate features improved mechanical properties. The highest compressive strength (40.30 MPa) and splitting tensile strength (3.78 MPa) are observed at a 50％ substitution rate of recycled coarse aggregate, showing an improvement of 23.5％ and 9.6％ respectively over the concrete prepared by natural aggregate. Under the 75％ substitution rate of recycled coarse aggregate, the flexural strength of concrete is 6.54 MPa, showing a 5.7％ increase compared with the concrete prepared by natural coarse aggregate. [Conclusions] Adding an appropriate amount of BF and ITS significantly enhances the mechanical properties and durability of recycled concrete. The optimal mix design developed by this study not only effectively utilizes construction waste and industrial waste but also prepares recycled concrete with superior performance, providing valuable references for the development of green building materials.