[Objective] With the increasing proportion of new energy, traditional grid-following (GFL) control based on phase-locked loop (PLL) synchronization gradually exhibits inherent stability limitations in weak grid conditions. Meanwhile, grid-forming (GFM) control with self-synchronizing source characteristics has emerged as a hot solution. However, existing research predominantly focuses on the voltage regulation or synchronization stability of GFM control, with less attention to its frequency modulation capability and characteristics. [Methods] This paper systematically reviewed four mainstream GFM control methods, including droop control, virtual synchronous generator (VSG) control, matching control, and virtual oscillator control (VOC), explained their frequency modulation principles, and analyzed their advantages and disadvantages from the aspects of the control loop and application scenarios. On this basis, a grid-connected simulation model for new energy systems was built to conduct a simulation-based analysis of the frequency modulation response characteristics of different kinds of frequency modulation control across diverse scenarios. Finally, this study summarized challenges of GFM control in strategy optimization, parameter tuning, and multi-unit coordination, with the future development prospects pointed out. [Results] Droop control regulates the active power of generating units by responding to system frequency deviations, featuring advantages of the simple structure and strong grid strength adaptability. However, its lack of inertia support results in relatively weaker frequency modulation performance. On the basis of droop control, VSG control simulates the inertia response characteristics of conventional synchronous machines and can better suppress the change performance of system frequency. However, it faces challenges in parameter tuning, fault ride-through, and multi-unit coordination. Matching control utilizes the dynamic characteristics of DC capacitors to simulate the inertia properties of traditional synchronous machines and thus restrain change performance of system frequency, but it fails to provide sustained support in the frequency quasi-steady state. VOC generates frequency responses similar to droop control via oscillator dynamic equations that directly govern amplitude and frequency. However, it is difficult for its high output harmonics to satisfy grid connection requirements. [Conclusion] Virtual synchronous machine control has become the most promising research direction in GFM control due to its technical advantages of balancing frequency modulation performance and strong grid strength adaptability in participating in system frequency modulation. However, technical challenges including synchronization stability, fault ride-through, and coordinated control need to be tackled. In the future, in-depth research should be conducted on control strategies and parameter optimization, and multi-unit collaborated control to facilitate the large-scale application of GFM control.

[Objective] The cost of distribution network engineering is influenced by multidimensional factors such as scale and capacity, equipment and material costs, and geographical conditions. Traditional statistical methods (e.g., linear regression) struggle to handle high-dimensional nonlinear data effectively, while existing machine learning approaches, despite incorporating feature reduction techniques, still exhibit limitations. For instance, principal component analysis (PCA) sacrifices prediction accuracy for dimensionality reduction, and grey relational analysis (GRA) ignores feature interactions. Therefore, there is an urgent need for a prediction method that retains critical feature information while accounting for complex inter-feature relationships. This study integrated recursive feature elimination (RFE) with the random forest (RF) algorithm to develop a RFE-RF prediction model, aiming to resolve feature redundancy and nonlinear modeling challenges. [Methods] A technical framework of “feature selection-model construction-experimental validation” was adopted. For feature selection, the recursive feature elimination (RFE) method was employed, which iterated training models to gradually eliminate features with minimal predictive contributions, retaining an optimal feature subset. For model construction, the RF algorithm was utilized. Based on ensemble learning principles, RF constructed multiple decision trees and averaged their outputs, effectively mitigating overfitting and enhancing model robustness. RF was insensitive to noisy data and quantified feature importance, providing reliable feature ranking criteria for RFE. By embedding RFE into the RF training process, a closed-loop optimization workflow was established. [Results] Experimental validation used data from 190 distribution network engineering projects provided by a power grid company, covering 21 initial features such as voltage level, line length, and equipment costs. Categorical features were numerically encoded while preserving their original distribution characteristics. Through five-fold cross-validation and root mean square error (RMSE) optimization, the optimal feature subset was identified as 12 optimal feature subsets, including such key factors as line length, comprehensive cable price, and voltage level. Compared with traditional linear regression (LR), RF, and mutual information-based RF (MI-RF) algorithms, the RFE-RF algorithm achieves a mean absolute error (MAE) of 8.6579 and a mean absolute percentage error (MAPE) of 6.97％ on the test set, significantly outperforming other algorithms. The MAE of RFE-RF on the test set increases by only about 4.5％ compared to the training set, indicating lower overfitting risks and demonstrating that feature selection effectively enhances model stability. [Conclusion] Feature selection is pivotal for improving the accuracy of distribution network cost prediction. RFE dynamically eliminates redundant features through iterative processes, substantially reducing data dimensionality and noise interference. The RFE-RF model combines high precision with strong interpretability, reduces MAE significantly compared to traditional models, and clearly quantifies the impact weights of individual features on costs. This study marks the application of combining RFE and RF in cost prediction for distribution network engineering, addressing challenges in feature interaction and redundancy filtering and providing a new paradigm for data modeling in complex engineering systems. The model serves as a precise cost prediction tool for power grid enterprises, aiding investment decisions and cost control, thus advancing intelligent and refined construction of distribution networks. Moreover, it reveals the impact mechanism of feature selection on the generalization capability of machine learning models, offering practical references for feature optimization in high-dimensional nonlinear datasets.

[Objective] With the global energy transition and the rapid development of clean energy, the penetration rate of high-penetration photovoltaic (PV) sources in distribution networks is increasing. However, PV output power exhibits significant fluctuations and uncertainty due to factors such as solar irradiance and temperature. When a large number of such sources are integrated into distribution networks, they can cause voltage fluctuations, frequency variations, and other issues, presenting significant challenges for power outage fault prediction. Traditional fault prediction methods struggle to accurately capture fault characteristics in complex distribution networks with high PV penetration, leading to reduced prediction accuracy and efficiency, which fails to meet the stability requirements for distribution network operation. [Methods] To improve prediction accuracy and efficiency, this study proposed a fault prediction method for distribution networks with high PV penetration. First, a PV-integrated grid model was built to analyze the impact of PV sources on fault current characteristics in distribution networks. This model clarified how PV sources influence fault current magnitude and distribution under different operating conditions, providing a theoretical basis for subsequent fault zone identification. Next, potential outage zones were inferred by combining grid topology and load imbalance features. The grid topology reflected the connectivity of components, while load imbalance indicated regional load variations. By integrating these factors, the method more accurately localized the fault zone. In addition, power flow entropy was introduced to assess whether circuit loads were in a critical state. Key fault-related power flow features were then extracted from the identified zones. These features were fed into an optimized SA-SAE for training, allowing the system to automatically learn underlying patterns from large datasets and achieve precise outage prediction. [Results] Experimental results demonstrate that the proposed method achieves high prediction accuracy in fault localization for distribution networks with high PV penetration, correctly identifying fault zones (sections 3-6 of the K5-K8 lines) and fault types. Moreover, the average prediction time is only 2.236 seconds, significantly outperforming comparative methods in both accuracy and efficiency. [Conclusion] By comprehensively considering PV integration effects, grid topology, load characteristics, and leveraging power flow entropy and SA-SAE, the proposed method enables high-precision and high-efficiency outage prediction in distribution networks. This method not only enhances prediction accuracy and timeliness, reducing outage risks and economic losses, but also provides robust support for grid planning, operation, and maintenance. It ensures stable distribution network operation and facilitates large-scale integration of clean energy.

[Objective] With the increasing demand for application of unmanned aerial vehicles (UAVs) in complex scenarios such as power grid inspection and emergency rescue, the limitations of single UAV in task execution have become increasingly prominent. Multi-UAV formation can effectively improve inspection efficiency and expand operation coverage, but significant challenges remain in formation maintenance, collaborative trajectory optimization, and environmental adaptability to complex environments during practical application. An optimal control method that integrated virtual spring forces with the hp-adaptive pseudospectral method was proposed to address the difficulties of formation maintenance and path optimization during large planar maneuvers of UAV swarms, thus enhancing the stability, flexibility, and disturbance resistance of collaborative flight of UAV formations and providing technical support for high-demand scenarios for UAVs such as power grid inspection. [Methods] First, a multi-UAV system dynamics model was built, and a virtual spring mechanism was incorporated into the formation control system to realize flexible constraints and elastic self-adjustment between UAVs. By combining the virtual spring method with the traditional leader-follower method, a formation strategy that could balance rigid support and adaptive adjustment ability of formations was designed. On this basis, the hp-adaptive pseudospectral method was then applied to solve the optimal control problem of UAV formations. By discretizing state and control variables at Legendre-Gauss nodes and constructing global interpolation polynomials, the trajectory optimization problem was transformed into a nonlinear programming (NLP) problem, with constraints such as dynamics, energy consumption, and velocity combined to conduct a high-precision numerical solution. In simulation experiments, a typical four-UAV diamond formation was set up, and the algorithm′s adaptability to different terrains, wind disturbances, and mission requirements was comprehensively explored. [Results] Simulation results show that the proposed virtual spring-based hp-adaptive pseudospectral method can realize smooth formation turning and velocity control. During a 90° large maneuver, UAVs can not only satisfy multiple constraints such as path deflection and speed change, but also maintain a stable formation. Compared with traditional leader-follower and artificial potential field methods, the new method demonstrates significant advantages in position error, formation maintenance, and wind resistance. Under 10m/s strong wind, the formation stability of the proposed method exceeds 70％, showing significant advantages over its competing algorithms. 3D terrain simulations and real flight tests further validate the algorithm′s adaptability and robustness, and the method still maintains lower formation deformation rates and trajectory tracking error under multiple terrains such as hills, mountains, and canyons, with the features of reasonable energy consumption control and strong engineering practicability. [Conclusion] By innovatively integrating the virtual spring elastic constraint with the hp-adaptive pseudospectral method, an optimal control technique for UAV formation trajectory planning in complex environments was proposed. The rigidity constraint limitations of traditional formation methods are overcome, flexible maintenance and adaptive adjustment of formations are realized, and the accuracy and efficiency of collaborative trajectory optimization are significantly improved by the method. The research results provide an efficient and reliable technical path for collaborative flight of UAV swarms in demanding tasks such as power grid inspection and emergency rescue. Future studies may further increase the method′s application potential in multi-formation collaboration and complex obstacle environments, promoting the intelligent and practical development of UAV formations.

[Objective] Interior permanent magnet (IPM) motors are widely adopted as submersible motors in oil well applications due to their structural stability, high efficiency, and superior power factor. However, the constrained wellbore diameter and elevated ambient temperatures in deep-well environments impose stringent requirements for enhanced torque density and anti-demagnetization capability of IPM motors. To address these challenges, a novel sine-shaped permanent magnet (PM), synthesized from flat and arched PM, was adopted in this study. The adopted PM optimizes rotor space utilization above conventional flat PM, thereby increasing permanent magnet volume and d-axis permanent magnet thickness, improving torque density and anti-demagnetization capability of IPM motors. [Methods] Maintaining constant dimensions of the flat PM, the finite element analysis (FEA) was employed to systematically evaluate the effects of arched PM sagitta on short-circuit current, anti-demagnetization capability, and no-load and on-load electromagnetic performance. Moreover, the equivalent ring method was implemented to quantify the maximum stress of the rotor core under various sagittas, ensuring the mechanical integrity of the optimized rotor structure. [Results] Although the short-circuit current of the IPM motor will increase as the sagitta increases, its permanent magnets exhibit stronger anti-demagnetization capability. While increasing the sagitta will increase the maximum stress of the rotor core, it is much lower than the yield stress of the core material, meeting practical needs sufficiently. Moreover, the effect of a smaller sagitta on the reluctance torque of the IPM motor can be ignored. When the sagitta is greater than 3 mm, the maximum reluctance torque decreases significantly as the sagitta increases, while the total output torque keeps increasing and torque ripple decreases. [Conclusion] Taking into account the overall influence of the sagitta on the motor′s performance, a suitable sine-shaped PM size was selected, and a prototype was manufactured and tested. The experimental results are in good agreement with the 2D FEA simulation results, verifying the accuracy of the simulation analysis, which provides a new approach for the rotor design of the IPM motors.

[Objective] With the expanding user group of social software, multi-label annotation has been increasingly adopted for text information. How to analyze the behavior and psychology of the user group through data mining of multi-label text information has become a research hotspot. A data mining algorithm for multi-label implicit knowledge based on a deep topic feature extraction model was utilized to enhance text classification accuracy and data mining efficiency. [Methods] To deeply understand the implicit knowledge in text information, the socialization, externalization, combination, and internalization (SECI) theory was employed to convert the implicit knowledge into explicit knowledge. The short-term memory capability of recurrent neural networks was utilized to improve the conversion efficiency. Considering the complexity of text information, local and global features were analyzed separately, and feature fusion was used to improve data mining efficiency. Due to the strong correlation between the context of text information, the gate mechanism of the long short-term memory (LSTM) model was applied to extract contextual dependencies, while the unsupervised latent Dirichlet allocation (LDA) topic model was selected to model the topic structure of the text to mitigate standard differences from manual labeling. Combining LDA-derived global features and LSTM-derived local features, feature stitching was performed to reduce information loss during the feature extraction. A theme controller was introduced to narrow down the inference scope, which obtained more effective text features. Simultaneously, a Gaussian decoder-based contextual topic layer was constructed to calculate the conditional probability matrix of each vocabulary under a given topic, and a Gaussian mixture decoder was used to obtain the conditional probability of the vocabulary. Topic modeling optimization and content expansion were achieved through a Gaussian mixture decoder. Finally, multi-label classification was implemented using the Softmax function to calculate label probabilities. [Results] During model training, perplexity was used as a criterion for evaluation. The proposed model exhibited better perplexity than the control groups (LDA topic model and LSTM model), demonstrating the effectiveness of feature concatenation combining the LDA topic model and LSTM model. By comparing with NVDM, LSTM, LDA, and VAETM models, with precision and recall as evaluation metrics, the proposed model improves precision and recall by 5.05% and 2.75%, respectively. [Conclusion] The comparative experimental results show that the proposed model can significantly improve the performance of text classification. Compared with the LDA topic model and the LSTM model, it outperforms in processing multi-label texts. It can efficiently mine the implicit knowledge in multi-label text data, providing an efficient and accurate solution for tasks such as text classification, semantic analysis, and information retrieval.

[Objective] Due to the influence of the limited regulated direct current (DC) power supply, amplitude control of each variable of the chaotic system, that is, variable compression, has become an essential prerequisite for chaotic circuit design and implementation. Currently, geometric control of the attractors of chaotic systems, such as amplitude control and bias control, is a hot research direction in the field of chaotic systems. Based on existing methods, a new amplitude control method was proposed in this paper in the expectation of exploring more potential applications of chaotic systems. [Methods] A five-dimensional chaotic system was developed, and its chaos was verified by using a three-dimensional phase diagram and Lyapunov exponents. After the absolute values of state variable-u in the two equations of the system were taken, two new switched chaotic systems were obtained. Compared with the phase diagram of the chaotic system, the amplitudes of these two new systems changed, and their shapes were highly similar, namely that global amplitude control was achieved. After the absolute value of-u in the second equation was taken, it became a memristive chaotic system. The existence of the memristor was verified by the pinched hysteresis loops of three frequencies. Further analysis of the memristive chaotic system was carried out. By adding the parameter k to the three nonlinear terms of the memristive chaotic system, it was found that the average amplitudes of the attractor on five dimensions changed accordingly, which indicated that the memristive chaotic system had a global amplitude control parameter. The existence of multi-stability in the memristive chaotic system was verified by the Lyapunov exponent spectrum changed with the memristive parameter a. Moreover, the absolute mean value of the signal and the phase diagram changed with a proved that when an appropriate value of the memristive parameter a was selected, global amplitude control could also be achieved. [Results] The simulation circuit equations, equivalent circuit diagram of the memristive chaotic system, and the simulated phase diagram of the chaotic system on the oscilloscope are highly similar to the computer simulation results, which indicates that the chaotic circuit design is of reliability. [Conclusion] The proposed five-dimensional chaotic system has strong chaotic property. The switching system with switching amplitude variation was proposed, providing a new direction for the research of memristive chaotic systems. In future work, it is possible to attempt to use a curved surface as the switching surface. Additionally, through computer simulation experiments, whether the phenomenon of switching amplitude variation widely exists in memristive chaotic systems will be further studied, and further work will be carried out to explore the principle of its existence. The phase diagram on the oscilloscope is highly consistent with the computer simulation experiment in five dimensions. The system has the characteristics of high dimensionality, strong chaos, and switching amplitude control, which make it have good application prospects in engineering.

[Objective] Industrial control protocol parsing is a critical component of industrial internet security. However, traditional methods suffer from poor universality and low accuracy. These issues lead to a low efficiency in protocol parsing, making it difficult to meet the demands for high precision and adaptability in real-world industrial scenarios. [Methods] A deep learning-based reverse engineering method was proposed for industrial control protocols by integrating a bidirectional encoder representations from transformers (BERT) pre-trained model, a bidirectional long short-term memory (BiLSTM) network, and conditional random fields (CRF). The goal is to enhance the universality and accuracy of protocol parsing, thereby providing technical support for security analysis and vulnerability mining in industrial control systems. First, the BERT pre-trained model was employed to dynamically encode industrial control protocol data into high-dimensional word vector representations, so as to capture the semantic information of the protocol data. Leveraging the powerful contextual understanding capabilities of BERT, the model effectively handled the complexity and diversity of protocol data. Subsequently, a BiLSTM network was utilized to model the relationships between protocol data as well as between protocol data and label data. The BiLSTM network captured long-range dependencies within the protocol data, enabling a better understanding of the structure and semantics of the protocol. Finally, CRF were introduced as constraints to optimize the prediction of protocol formats and semantics. By incorporating transition probabilities between labels, CRF further enhanced prediction accuracy and consistency. The combination of the BERT pre-trained model, BiLSTM network, and CRF enabled the format extraction and semantic analysis of industrial control protocols. Additionally, the proposed method was optimized for large-scale protocol data, which ensured efficiency and stability in complex industrial scenarios. [Results] Experiments were conducted on three typical industrial control protocols. The results demonstrate that the proposed method achieves an accuracy of over 96％ in both format extraction and semantic analysis, outperforming traditional methods. The method exhibits high adaptability and accuracy across different protocols, effectively identifying field boundaries and semantic information. [Conclusion] The proposed method significantly improves the universality and accuracy of industrial control protocol parsing, providing reliable technical support for security analysis in industrial control systems. Future work will focus on further optimizing the model, expanding its application scenarios, and enhancing its practicality.

[Objective] With the continuous expansion of oil and gas pipeline transportation, the importance of pipeline safety inspection has become increasingly prominent. Stress concentration at pipeline defects is the main cause of crack propagation and fracture accidents. However, existing detection methods struggle to achieve quantitative stress evaluation. [Methods] This study proposed a pipeline stress detection method based on dual-field stress-magnetic coupling. By incorporating changes in the Jiles-Atherton (J-A) model parameters under different pipeline stress states, a magnetic stress detection model was built. The effects of elastic stress, plastic strain, and external magnetic fields on magnetization intensity and magnetic signal characteristics were systematically analyzed. The study was grounded in the principles of magnetic stress detection, the J-A model, and magnetic charge theory. By examining the influence of stress at different stages and external magnetic fields on magnetization intensity and magnetic signals, the relationship between hysteresis loops and magnetization intensity under varying conditions was established. In addition, the variation patterns of axial and radial signals under different stress and magnetic field conditions were identified. A proportional coefficient was introduced to develop a dual-magnetic field stress detection model, and separate models for elastic and plastic stress detection were built. Finally, experiments were conducted to verify the theory. Equivalent magnetic field strength formulas for the elastic stress and plastic strain stages were derived, clarifying the variation laws of the pinning coefficient k, shape coefficient a, and domain wall coupling coefficient α with stress. Experimental validation was conducted using X80 pipeline steel specimens subjected to tensile loads ranging from 10 to 80 kN and external magnetic fields from 0 to 10A/m, with magnetic signal characteristics measured. [Results] The axial component of magnetic signals under different magnetic fields and stress levels exhibits distinct peaks, with peak positions remaining stable despite variations in external fields or stress. Tangential peaks increase with the external magnetic field, aligning with theoretical calculations. Experimental data indicate that the model closely matches measured results under high stress, with minimal error, while low-stress scenarios show slight deviations due to parameter fitting limitations. [Conclusion] In the elastic stage, tensile stress causes the hysteresis loop to rotate counterclockwise initially and then clockwise. Magnetization changes significantly under weak magnetic fields, whereas stress effects become negligible under strong fields. During the plastic stage, plastic strain reduces the slope of the magnetization curve, and both the initial magnetization curve and hysteresis loop rotate clockwise. Magnetization intensity is proportional to magnetic signals, with the ratio of strong magnetic signals to magnetization intensity serving as a proportionality coefficient dependent solely on defect size. The dual-magnetic field stress detection model demonstrates high accuracy under high stress, confirming its capability for stress detection. This study innovatively integrates the dual-magnetic field method with J-A theory, proposing a proportional coefficient-based model for separating elastic and plastic stresses. The approach resolves the issue of overlapping defect and stress signals in traditional methods, providing a high-precision, quantifiable technical solution for stress detection at pipeline defects. This advancement holds significant value for preventing pipeline failures and ensuring safe energy transportation.

[Objective] 6061 aluminum alloy is widely used because of its good comprehensive properties. However, the quality of its welded joints generally has certain limitations. This study aims to effectively improve the quality of 6061 aluminum alloy welded joints by using the method of laser shock peening, deeply explore the changes of mechanical properties and microstructure of welded joints before and after laser shock peening, and analyze the internal influence mechanisms, so as to provide a solid theoretical basis and practical guidance for the optimization of aluminum alloy welding process. [Methods] A 6061 aluminum alloy welded joint was selected as the research object, and its surface was treated by laser shock peening technology. In the process of treatment, the parameters of laser frequency, shock range, pulse width, and overlap rate of laser pulses were strictly controlled. The influence of laser energy on 6061 aluminum alloy welded joints was studied. The mechanical properties of welded joints before and after laser shock peening were analyzed, such as tensile strength and hardness. At the same time, the changes of microstructure characteristics such as grain size and shock layer thickness at the weld were observed and compared by means of microstructure analysis technologies, including optical microscopy, scanning electron microscopy, and electron backscatter diffraction (EBSD). [Results] First of all, in terms of the relationship between laser energy and tensile strength of welded joints, there is a clear positive correlation. Specifically, with the gradual increase in laser energy, the tensile strength of welded joints also increases steadily. Secondly, the detection of the hardness of the weld surface shows that the hardness is significantly improved after laser shock peening, and the increase is about 23％. Finally, from the microstructure point of view, the thickness of the laser shock layer changes significantly, greatly increasing from the initial 15.83μm to 30.77μm, which indicates that the laser shock has a deep impact on the surface of the material. At the same time, the grain size of the weld center also changes significantly, decreasing from the original 33.68 μm to 14.5 μm. The grains obviously become finer, namely that the microstructure is optimized. [Conclusion] Based on the above research results, it can be concluded that laser shock peening technology shows excellent effect in the treatment of 6061 aluminum alloy welded joints. The high energy generated on the surface of metal materials can effectively reduce the adverse effects of plastic deformation on the surface and interior of materials and promote grain refinement, which is the key factor to improve the mechanical properties of welded joints. Through laser shock peening, the tensile strength and hardness of welded joints are effectively improved, which not only helps to improve the reliability and durability of 6061 aluminum alloy welded structures in practical applications but also provides strong technical support for further expanding the application range of aluminum alloys in high-end manufacturing. In the future, the optimal process parameter combination of laser shock peening can be further studied in order to improve the quality of 6061 aluminum alloy welded joints more accurately and efficiently and promote the continuous development and innovation of aluminum alloy welding technology.

[Objective] In the context of the rapid development of world industrialization, the indiscriminate discharge of dye wastewater poses a threat to the ecological environment and human life. Therefore, seeking an efficient, clean, and economical wastewater treatment method has become a research hotspot. In recent years, a photo-Fenton catalytic technology has shown great advantages in treating organic dye wastewater. BiOBr is a photocatalyst with a good visible light response, a strong light stability, and a layered structure. However, it has the problem of easy recombination of photogenerated electron-hole pairs. Monometallic irons such as Fe₂O₃, Fe₃O₄, FeOOH, and nanoscale zerovalent iron exhibit excellent performance in Fenton catalytic activity. Furthermore, their photo-Fenton catalytic activity has also been acknowledged. [Methods] Coupling BiOBr with iron-based oxides with a good Fenton catalytic activity or modifying BiOBr can effectively improve the treatment efficiency of organic wastewater. To further improve the efficiency of photo-Fenton catalytic technology in treating organic pollutants in water, a Fe₂O₃/BiOBr composite photo-Fenton catalyst with a Z-scheme heterojunction was prepared by precipitation and calcination methods. The composite catalyst was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), and the photo-Fenton catalytic activity and mechanism of the composite catalyst were studied using Rhodamine B (RhB) as an organic pollutant model. [Results] The results indicate that the Fe₂O₃/BiOBr composite photo-Fenton catalysts all exhibit excellent photo-Fenton catalytic activities compared to the individual catalysts, and the 1.2％ Fe₂O₃/BiOBr has the highest photo-Fenton catalytic activity (99.59％, 60 min), which is about 24.8 and 3.6 times higher than those of pure Fe₂O₃ and BiOBr, respectively. Electrochemical testing and the radical trapping experiment show that the 1electrons in the composite catalyst flow from the conduction band of BiOBr to the valence band of Fe₂O₃. This achieves effective separation of photogenerated electron-hole pairs and promotes the regeneration of Fe²⁺, thereby improving the photo-Fenton catalytic efficiency of the composite catalyst. [Conclusion] The composite catalyst 1.2％Fe₂O₃/BiOBr achieves complete degradation of the organic pollutant in all five cycles of the catalytic degradation experiments with no decrease in catalytic degradation efficiency. This demonstrates that the composite catalyst possesses excellent catalytic activity and stability, which makes it a promising candidate for application as a photo-Fenton catalyst in green, economical, and efficient industrial wastewater treatment processes.

[Objective] In recent years, the development of lithium-ion batteries have encountered bottlenecks such as slow energy density improvement, high cost and narrow temperature adaptation range. Potassium-ion batteries featuring low cost and high energy density have become the ideal choice for the next generation of large-scale electrochemical energy storage systems. Phosphate fluoride (KVPO₄F) serves as the first-choice cathode material for potassium-ion batteries due to its solid three-dimensional framework and high operating voltage. However, the repeated embedding/removal of large potassium ions in the charge and discharge process will cause structural pulverization to KVPO₄F, resulting in rapid capacity decay and poor cyclical stability. Moreover, the structure formed by the covalent bond of the coordination polyhedron restricts the electron transfer mode, greatly hindering the dynamics performance of KVPO₄F cathode material, and resulting in poor magnification behavior and low actual capacity. The modification of KVPO₄F material is usually studied by such strategies as element doping, carbon coating, and morphology engineering to improve the capacity, magnification and cyclical stability of KVPO₄F cathode material, and thus enhance the potassium storage performance. However, due to the imbalance between lattice spacing, crystal face exposure and V³⁺ content, the capacity, magnification, and cyclical stability are difficult to be improved simultaneously. The synthesis of KVPO₄F cathode material usually consists of two successive heat treatment steps, including the preparation of the VPO₄ precursor and the secondary calcination of VPO₄ mixed with KF to produce KVPO₄F. Therefore, the crystal structure of VPO₄ is bound to affect the particle size and crystal face orientation of KVPO₄F, thus affecting the potassium storage stability of KVPO₄F. [Methods] A series of VPO₄ materials were prepared by the sol-gel and high-temperature annealing method, and the effects of different VPO₄ materials on the lattice and electrochemical properties of the final product KVPO₄F were studied. [Results] The results show that VPO₄ prepared at different temperatures can significantly affect the lattice exposure intensity, lattice spacing and V³⁺ content of KVPO₄F. As the temperature rises from 700℃ to 800℃, the lattice exposure intensity, lattice spacing and V³⁺ content increase first and then decrease. When VPO₄ annealed at 750℃ is employed as the precursor, the prepared KVPO₄F has the most intense lattice plane exposure, the largest lattice spacing and the highest V³⁺ content, which ensures excellent structural stability, ion migration and ion storage quantity during the charge and discharge process. The electrochemical property test shows that after 30 cycles at 0.2 C (1 C=131 mA/g), the specific capacity of KVPO₄F is 57.3 mAh/g, much higher than that of the control sample under the same conditions. Additionally, the reversible specific capacity of KVPO₄F at 0.2 C, 0.5 C, 1 C, and 2 C is 62.1, 53.8, 44.6, and 30.6mAh/g, respectively. [Conclusion] Based on VPO₄ regulation, this study determines the effect of precursor VPO₄ on the microstructure of the final product KVPO₄F, and reveals the internal mechanism of improving electrochemical properties, laying a sound foundation for obtaining high-capacity KVPO₄F cathode material.

[Objective] The anisotropy and isotropy of thermal transport are fundamental properties of materials, which are crucial in practical applications. However, current research on tuning the transition from anisotropic to isotropic thermal transport primarily relies on structural design or material processing. The methods are time-consuming, costly, and irreversible, which severely limits flexibility of the properties in practical applications. Therefore, a scheme was proposed to regulate the thermal transport properties of two-dimensional borophene by using an external electric field, aiming to explore a new method for stable and reversible regulation without altering the atomic structure of the material. [Methods] First-principles calculations were combined with the phonon Boltzmann transport equation to systematically investigate the effect of an external electric field on the thermal transport properties of borophene. The underlying physical mechanisms were revealed systematically by quantifying the regulatory effects of electric field strength on phonon lifetime, thermal conductivity, and anisotropy, and the ratio of thermal conductivities in two in-plane directions (x and y directions) was used as an indicator of the changes in anisotropy. [Results] Under the influence of an external electric field, the lattice thermal conductivity of borophene in both in-plane directions increases significantly and gradually peaks with a maximum enhancement factor of 2.82. Meanwhile, the intrinsic anisotropy ratio is boosted to a maximum value of 2.13. As the electric field strength increases further, the thermal conductivity drops rapidly, and the anisotropy exhibits oscillating decay. When the electric field strength increases to 0.4 V/Å, the thermal conductivity is dramatically reduced. Nearly isotropic thermal transport characteristics are demonstrated when the anisotropy ratio decreases to 1.25. Further analysis reveals that this abnormal transition from anisotropic to isotropic thermal transport is fundamentally due to the large enhancement and suppression of phonon lifetime at moderate and high electric field strengths, respectively, which acts as an amplifying or reducing factor for thermal conductivity. [Conclusion] Phonon lifetime can be modulated by an external electric field, achieving stable and reversible precise regulation of the thermal transport properties of two-dimensional borophene without altering its atomic structure. This approach effectively overcomes the limitations of traditional regulation methods and provides a new theoretical and technical pathway for the precise regulation of phonon thermal transport anisotropy, showing a broad application prospect in such fields as thermal management of nanoelectronics and thermoelectric energy conversion.

[Objective] Cracks in aero-engine turbine guide vanes are typically repaired using wide-gap brazing. However, pores usually appear during the formation of wide-gap brazed joints, which can lead to a decline in their high-temperature mechanical properties. To suppress the formation of pores, a certain pressure is applied during brazing. Nevertheless, the effects of brazing pressure on the microstructure and mechanical properties of wide-gap brazed joints remain unclear. [Methods] In this study, wide-gap brazed joints were prepared under different brazing pressures. The effects of brazing pressure on the microstructure and mechanical properties of the joints were investigated through tensile testing, microhardness characterization, fracture morphology observation, energy dispersive spectroscopy (EDS) analysis, and X-ray diffraction (XRD). [Results] The experimental results show that at a constant brazing temperature, the tensile strengths of the wide-gap brazed joints under brazing pressures of 10, 20, and 50 kg with the mass ratio of the brazing filler metal to the base metal as 40:60 are 436.57, 411.76, and 381.95 MPa, respectively, namely that the tensile strength of the joints decreases with increasing brazing pressure. Microhardness and EDS analysis of the joint fracture surface reveal that as the brazing pressure increases, the concentrations of melting point depressant elements and active elements at the fracture surface become higher, and the microhardness significantly increases. This indicates that higher brazing pressure leads to uneven microhardness distribution in the joint, inducing significant stress concentration and thereby reducing joint strength. XRD results confirm that the applied brazing pressure causes noticeable lattice distortion in the joint and base material. Higher brazing pressure results in greater lattice distortion, which blocks the diffusion channels of melting point depressant elements and active elements, hindering their diffusion. This leads to the accumulation of these elements in the joint and base material, thereby causing uneven microhardness distribution and a decline in joint strength. Post-welding heat treatment or increasing the brazing temperature can alleviate lattice distortion, enhance element diffusion, and improve the mechanical properties of the joint. [Conclusion] Applying a certain pressure during the preparation of wide-gap brazed joints helps suppress the formation of internal pores. However, the contradictory effects of brazing pressure and temperature on lattice distortion need to be carefully considered. Excessive brazing pressure can hinder element diffusion, while increasing the brazing temperature can enhance element diffusion, reduce the unevenness of microhardness distribution, and ultimately improve the mechanical properties of the joints.

[Objective] With the rapid development of long-span suspension bridges, wind-induced vibration has gradually become a crucial factor affecting their safety and comfort. Small horizontal-axis wind turbines installed on bridges can not only effectively suppress vortex-induced vibration but also provide wind energy for powering ancillary facilities. However, the impact of small horizontal-axis wind turbines on bridges has not been comprehensively and systematically studied, especially their specific influence on bridge buffeting response. Therefore, this study aims to explore the influence of small horizontal-axis wind turbines on bridge buffeting response and assess the effects of different wind turbine layout schemes on bridge dynamic response, so as to provide a theoretical basis and practical guidance for control of wind-induced vibration of bridges by wind turbines. [Methods] This study took the typical flat box girder of the Great Belt Bridge in Denmark as the research object and employed such means as wind tunnel tests, finite element analysis, and harmonic superposition, combined with the actual wind environment and structural characteristics of the Great Belt Bridge, to simulate and analyze the influence of wind turbines on bridge buffeting response. Static three-component force coefficients of the bridge with wind turbines installed were measured in wind tunnel tests, and time-history response data of the bridge subjected to wind loads were generated depending on relevant data. Based on the quasi-steady assumption and Davenport buffeting force model, combined with the finite element model, the dynamic response of the bridge under different wind speeds was calculated and simulated. Six different wind turbine layout schemes were designed during the research process, considering variations in parameters such as the rotation axis height and layout spacing of wind turbines, to investigate the effects of different layout schemes on the lateral and vertical displacement and acceleration responses of the bridge. [Results] The results indicate that the installation of small horizontal-axis wind turbines increases the displacement and acceleration responses of the bridge to a certain extent. However, by selecting appropriate wind turbine layout schemes, it is possible to control vortex-induced vibration with a small effect on the structural safety and comfort of the bridge. The overall increase in lateral displacement of the bridge tends to decrease as the rotation axis height of the wind turbine blades decreases. For vertical response, the smallest increase in vertical displacement occurs when the wind turbine layout spacing is three times the beam height. Furthermore, by fitting the static wind loads caused by wind turbines on the bridge, this study proposed estimation formulas for drag and lift unit loads of bridges with wind turbines installed, which could effectively assess the impact of wind turbines on bridges under different layout schemes. [Conclusion] The impact of small horizontal-axis wind turbines on bridge dynamic response can be reduced through reasonable layout parameters (such as rotation axis height and layout spacing) without significantly affecting the structural safety and comfort of the bridge. The installation height and spacing of wind turbines have significant impacts on the dynamic response of the bridge, and reasonable layout schemes should be selected according to the specific conditions of the bridge to ensure its structural safety and comfort. This study proposed a mathematical model relating to the layout spacing and rotation axis height of wind turbines and wind load data of the bridge, providing theoretical support for optimizing control of wind-induced vibration of bridges by wind turbines in the future.

[Objective] As the demand for resource recycling and sustainable development in the construction industry increases, the application of recycled aggregate in concrete structures has become a research hotspot. However, the mechanical properties of recycled aggregate differ from those of natural aggregate. The low tensile strength, elastic modulus, and high brittleness of recycled aggregate inevitably have a significant influence on the bending performance of prestressed self-compacting recycled concrete (PSRC) beams. To clarify the feasibility of employing recycled aggregate in PSRC beams, this study discussed and analyzed the bending performance differences between PSRC beams and prestressed normal concrete (PNC) beams. [Methods] This paper adopted the finite element analysis method for the study. Firstly, the finite element models of PSRC beams and PNC beams were built based on the ABAQUS software. Meanwhile, the correctness and reliability of the built models were verified by comparing the failure modes, load-deflection curves, and limit loads of the simulated specimens with those of the test specimens. Secondly, on the basis of model verification, a systematic comparison and analysis were conducted on the performance indicators such as cracking load, limit deflection, flexural bearing capacity, and tensile reinforcement strain of PSRC beams and PNC beams. Additionally, based on the maximum and average strain of the tensile reinforcement at the cracking point, the coefficient of uniformity of the tensile reinforcement was corrected, and a calculation formula for the maximum crack width of PSRC beams was established. The applicability and accuracy of the formula were verified. [Results] Under the same reinforcement ratio, the cracking load of PSRC beams is smaller than that of PNC beams, and the difference in crack resistance performance between the two types of concrete beams gradually decreases with the increasing reinforcement ratio. Under the reinforcement ratio between 0.10％ and 2.24％, the limit deflection of PSRC beams is 4.04％-19.03％ higher than that of PNC beams. Then, as the reinforcement ratio continues to increase, the limit deflection difference between the two types of concrete beams gradually decreases until it is basically zero, which means the influence of the material properties of concrete on the deformation capacity of the component gradually decreases with the rising reinforcement ratio. The flexural bearing capacity of PSRC beams and PNC beams differs by no more than 3％, indicating that the existence of recycled aggregate has little effect on the flexural bearing capacity. Under the action of the same load, when there is a crack in concrete, the strain curve of the tensile reinforcement of PSRC beams is slightly lower than that of PNC beams. Due to the earlier cracking of PSRC beams, the tensile stress transmitted by the tensile zone concrete is borne in advance by the longitudinal reinforcement at the crack, resulting in larger tensile reinforcement strain at the cracking point of PSRC beams than that of PNC beams. [Conclusion] This study proposed a new method for determining the maximum crack width of PSRC beams based on the strain simulation data at the cracking point to correct the coefficient of uniformity of the tensile reinforcement. The maximum crack width was calculated by adopting the proposed new calculation method and the formula in GB50010—2010. It is found that the calculated values of the proposed formula are in sound agreement with the measured values, and the predicted maximum crack width by the proposed formula is more accurate than that by the formula in GB50010—2010. This paper provides a reference basis for the revision of subsequent standards.