[Objective] Under the impetus of the carbon peaking and carbon neutrality goals, the high proportion of renewable energy grid connection has weakened system inertia and damping characteristics, posing a serious threat to grid stability. Although grid-forming virtual synchronous generator (VSG) control technology can actively provide inertia support for the grid, the complex nonlinear characteristics of renewable energy systems cause traditional VSG control to face risks of instability in angular frequency and output voltage under non-ideal operating conditions, resulting in suboptimal control performance. To address this issue, this paper proposed a grid-forming converter control strategy based on passivity-based control, overcoming the limitations of traditional linear control methods to enhance system dynamic response performance, interference resistance, and robustness. [Methods] This paper adopted a nonlinear control design framework. According to VSG control principles, the rotor motion equation of a synchronous generator was employed to achieve active frequency regulation, and the excitation system was used to achieve reactive voltage control. A Hamilton system model incorporating grid-forming control was built. Based on the core principles of passivity-based control theory, the Hamilton model was mathematically transformed into a dissipative Hamilton standard form with port characteristics. This model inherently possesses advantages for stability analysis, providing a theoretical foundation for controller design. In addition, based on the system′s stable operation requirements, the desired equilibrium operating point was set. To accelerate the dissipation of system energy toward the desired equilibrium point, effectively suppress oscillations, and enhance convergence speed, a damping term was introduced. Ultimately, the active and reactive control laws applicable to grid-forming converters were derived, achieving global asymptotic stability of the nonlinear system. [Results] Simulation test results show that under non-ideal conditions such as power change, grid voltage imbalance, short-circuit fault, and load variation, the grid-forming converter control strategy based on passivity-based control significantly outperforms traditional VSG control in terms of system angular frequency stability. The amplitude of frequency fluctuations is significantly reduced, and the time required to recover to the desired value is greatly shortened. The overshoot of the output voltage is reduced, the regulation process is smoother, and the voltage stabilizes faster, effectively enhancing voltage stability. [Conclusions] The proposed grid-forming converter stability control strategy establishes a nonlinear design framework based on the dissipative Hamilton model and designs a control strategy through energy shaping and damping injection. The derived control laws exhibit strong robustness and can accommodate complex operating conditions without requiring a precise system model. It effectively addresses the stability deficiencies and slow dynamic response of traditional VSG control when faced with system nonlinearities, overcoming the limitations of fixed linear control parameters. This provides a control strategy with strong interference resistance and fast dynamic response for renewable energy grid-connected systems, supporting the stable operation of new power systems.

[Objective] In the power system, transmission lines serving as a key component are often prone to failures caused by natural disasters such as lightning strikes due to their remote location and long distance. Lightning strokes on transmission lines in China account for about 50％ of the total accidents, which can cause tower insulation flashover, abnormal voltage, and even power supply interruption. Additionally, they can also damage electronic equipment and cause significant economic losses. An analysis of flashover voltage characteristics of 110 kV line insulators was conducted to improve the design of transmission lines and enhance their lightning resistance, thus strengthening the resistance of transmission lines to lightning strikes. [Methods] Based on the electric field theory and the energy conservation principle, the energy conservation equation of electrons in the electric field was constructed. Combined with the motion equations of three types of particles (positive attribute particles, negative attribute particles, and neutral particles) and Poisson′s equation, a mathematical model for lightning strokes on insulators was built. By adopting this model to calculate the lightning current overvoltage, the standard waveform of the current of the lightning impulse transmission line was obtained. At the same time, the relationship between the lightning resistance level of transmission lines and flashover voltage of insulator strings was analyzed. By analyzing the characteristics of lightning flashover voltage of insulators via overvoltage, the current value and lightning current overvoltage at the lightning strike point were obtained. Additionally, silicone rubber insulators for 110 kV lines were selected for experiments to simulate different parameters such as the call height, grounding resistance, lightning current waveform, and insulation distance, and analyze the influence of each parameter on the voltage waveform. [Results] The experimental results show that the voltage of the insulator gradually flattens after 12 μs when the insulator is subjected to different waveform lightning strikes. Specifically, the double exponential voltage peak is the largest, the oblique angle model has the best effect on voltage control, and the voltage waveform tends to be more stable under the insulation distance greater than 5 m. Compared with other methods, the voltage waveform analyzed by this method is closer to the actual situation, with an error of less than 1 kV and higher accuracy. [Conclusions] Under the tower height of 25 m, the insulator is the most sensitive to lightning impulse response, and the peak voltage increases with the rising grounding resistance. The oblique angle lightning current model has the best effect on voltage control, and the insulation distance greater than 5 m can improve the insulator′s ability to resist lightning impulse. In this study, mathematical models were combined with electric field theory, the influence of various parameters on insulators was comprehensively considered, and a more comprehensive and accurate voltage waveform and mathematical equation for insulator strings was established. The results provide scientific basis for the design of transmission lines, and hold engineering and reference significance for improving the lightning resistance of transmission lines and ensuring the safe and stable operation of power systems.

[Objective] Transmission lines operating in regions with strong lightning activity are highly vulnerable to lightning strikes. Double-circuit lines on the same tower feature compact structures and significant electromagnetic coupling effects, resulting in consistently high lightning fault rates. Existing lightning protection measures rely heavily on statistical experience and cannot effectively distinguish different types of lightning faults such as shielding failures and back flashovers, making precise protection difficult. Consequently, line tripping accidents remain a recurring problem, posing a serious threat to the safe and stable operation of the power grid. To address this issue, this study proposed a deep learning-based lightning fault identification method with high-accuracy automatic identification of shielding failure and back flashover, providing effective technical support for differentiated lightning protection in transmission lines. [Methods] A lightning fault simulation model for 220 kV double-circuit transmission lines on the same tower was developed using the electromagnetic transient simulation software ATP-EMTP to obtain overvoltage response data under different lightning current amplitudes and grounding resistance conditions. To address the non-stationarity and mode mixing of lightning signals, ensemble empirical mode decomposition (EEMD) was introduced, where Gaussian white noise was added to suppress mode mixing. The first four intrinsic mode functions (IMFs) were extracted to preserve the major characteristic components. Subsequently, frequency slice wavelet transform (FSWT) was applied to compute multi-band energy ratios, which, together with lightning current amplitude and grounding resistance, formed a multidimensional feature set. In terms of classification modeling, a CNN-LSTM-Attention deep learning architecture was proposed:CNN extracted spatial features, LSTM modeled temporal dependencies, and the Attention mechanism focused on critical information, thus enabling effective fusion and identification of complex signal features. [Results] Experimental results demonstrate the excellent performance of the proposed method in distinguishing shielding failure from back flashover. The overall identification accuracy reaches 98.6％, with both precision and recall exceeding 98.5％, and an F₁-score of 0.99. Compared with benchmark models such as SVM and CNN, the proposed method exhibits a clear advantage in identification accuracy. Results from 10 independent comparative experiments show an average accuracy of 99.7％ and a variance of 0.000 93, fully verifying its stability and reliability. [Conclusions] The lightning fault identification method based on EEMD-FSWT feature extraction and the CNN-LSTM-Attention fusion model effectively characterizes the time-frequency features of lightning signals of double-circuit transmission lines on the same tower, achieving high-accuracy differentiation between lightning shielding failure and back flashover. This method not only improves the accuracy and timeliness of fault diagnosis but also provides important data support for formulating differentiated lightning protection strategies. The research results have significant engineering application value and strong potential for wide application in reducing lightning-induced line tripping and ensuring the safe and stable operation of power systems.

[Objective] Relay protection equipment plays a crucial role in the operation of power systems. However, with the long-term operation of the equipment, aging and damage are inevitable, which will cause abnormal displacement data in relay protection equipment. If these abnormal data cannot be properly processed, the safe and stable operation of power systems will be affected. Therefore, how to effectively handle the abnormal displacement data of relay protection equipment has become a problem to be urgently solved. [Methods] An autonomous controllable fault-tolerant storage algorithm was proposed. Firstly, the state of the relay protection equipment was evaluated by this algorithm, with the potential disturbance factors analyzed. On this basis, the model predictive control (MPC) technology was adopted to predict the possible abnormal data. Based on the dynamic model of the system, decisions were made by MPC in advance via predicting the future state of the system. Then, the predicted abnormal data were corrected to restore the data to their original state. Meanwhile, the data elasticity theory and granularity rate were employed to calculate the compensation storage intensity. The data elasticity theory helped to measure the tolerance ability of the system in the face of faults, and the granularity rate was related to the data refinement degree. The accuracy and integrity of the data were ensured by the combination of the two. During the study, an experimental environment based on the above-mentioned algorithm was constructed, and the algorithm was tested by simulating the abnormal displacement data generated by the relay protection equipment in different working conditions. [Results] The algorithm′s efficacy was verified by the experiment. The fault-tolerant rate of the algorithm is above 0.89, which means that under a large amount of abnormal data, most of the data errors can be successfully handled by the algorithm. The proportion of memory required for storage is less than 20 MB, indicating that the algorithm occupies fewer memory resources for data storage. Under the data number of 10 000, the data transmission number is only 401, which reflects the high efficiency of the algorithm in data transmission. [Conclusions] It can be concluded from this study that the data fault-tolerance ability of relay protection equipment can be effectively enhanced by the proposed autonomous controllable fault-tolerant storage algorithm. The accuracy and integrity of the data are ensured by accurate prediction, correction of abnormal data and a reasonable storage strategy, thereby enhancing the ability of relay protection equipment to cope with equipment aging and damage. The reliability of relay protection equipment can be improved by the application of this algorithm in power systems to further ensure the safe and stable operation of power systems. The innovation of this study lies in the combination of MPC, data elasticity theory and the granularity rate to develop a brand-new fault-tolerant storage algorithm. This method of comprehensively adopting multiple technologies has unique advantages in handling the abnormal displacement data of relay protection equipment. By adopting the proposed algorithm, the data processing ability of relay protection equipment can be improved, and the risk of power system failures caused by data abnormalities can be reduced, which is of great significance for ensuring the safe and stable operation of power systems.

[Objective] Under the background of new power system construction, the randomness and fluctuation of the system are significantly exacerbated by the large-scale integration of high-proportion renewable energy and widespread popularity of flexible loads. Coupled with the continuously expanding power grid scale, control variables increase sharply, thus posing a severe challenge to the control strategies of traditional power grid voltage and tidal currents. The electrical distance between nodes is mainly relied on the existing power grid partitioning methods for reactive partitioning, which is difficult to adapt to the operation requirements for new power systems with drastic source-load changes. To this end, a power grid partitioning optimization method comprehensively considering multiple factors was proposed to lower the overall control difficulty of power grids under the penetration of high-proportion new energy and improve the autonomous operation capability of partitioning. [Methods] The core of this study is to build a set of partitioning index system and optimization model, and break through the traditional partitioning′s limitation of only focusing on the topological association. Additionally, the tightness of internal electrical connections and the degree of source-load matching were creatively considered, with the reactive partitioning indexes based on electrical distance and active partitioning indexes based on source-load matching constructed respectively. On this basis, the optimization model of power grid partitioning was built to minimize the reactive partitioning index, thus aiming to maximize the electrical tightness inside the partitioning and simplify reactive power and voltage control. Meanwhile, the key constraint that the active partitioning indexes satisfied the requirements was employed to limit the frequent interaction of active power between partitions, reduce the violent fluctuation of net loads within partitions, and ensure the source-load balance within partitions. At the same time, a bionic joint optimization algorithm was proposed, in which the global search ability of genetic algorithms and fast local refinement ability of firefly algorithms were fully used to efficiently solve the built nonlinear complex optimization model, improve the optimization speed, and avoid falling into the local optimal solutions. [Results] The standard IEEE 39-node system was adopted to verify the case example. The simulation results show that by adopting this algorithm, the source-load matching degree within partitions can be significantly improved, the fluctuation of net loads between and within partitions can be reduced, and unnecessary tidal current interaction can be decreased. Additionally, the difficulty of reactive control in the system can be lowered, the electrical tightness of nodes within the partitions can be enhanced, and the voltage and reactive regulation process within the partitions can be simplified by employing the algorithm. The proposed firefly-genetic bionic joint optimization algorithm exhibits excellent solution performance and can obtain the optimized partitioning scheme rapidly and effectively. [Conclusions] There are two main innovative points in this study. Firstly, the optimization objective of reactive control based on electrical distance and the constraint of active balance based on source-load matching are integrated in the power grid partitioning model, which overcomes the defect of insufficient adaptability of traditional methods to source-load changes. Secondly, an efficient and robust firefly-genetic bionic joint optimization algorithm was proposed to solve the partitioning model, thus effectively improving the optimization speed and accuracy. This algorithm provides a new technical way to solve the problem of partitioning operation control under the complex network structure of new power systems, and holds theoretical and practical significance for improving the safe and stable operation of power grids and promoting efficient consumption of new energy.

[Objective] With the explosive growth of network scale and complexity, traditional network operation and maintenance (O＆M) technologies face challenges such as low accuracy, severe noise interference, and difficulties in root cause localization when processing massive alarm data. Existing association rule algorithms such as Apriori and FP-Growth, fail to deeply analyze the correlations and hierarchical root causes in alarm data, resulting in low compression efficiency and high false positive rates. This paper aims to design an intelligent alarm data compression algorithm by introducing a graph convolutional network (GCN) to address the limitations of traditional methods in mining data correlations, suppressing noise, and analyzing multi-level root causes, thus enhancing the intelligence level of network O＆M and improving alarm processing efficiency. [Methods] To tackle the heterogeneity and redundancy of alarm data, a dynamic preprocessing mechanism based on sliding time windows was proposed. This mechanism constructed a high-precision alarm transaction database through time synchronization rules and redundancy removal operations. Next, the preprocessed alarm sequences were transformed into graph-structured data, where node feature matrices and adjacency matrices characterized alarm events and their correlations. A multi-layer GCN model was then designed: local convolution aggregated neighborhood node features, normalization techniques resolved structural imbalance in the graph data, and ReLU activation functions enhanced nonlinear feature extraction. Key parameter configurations included input feature dimensions, hidden layer structures, the Adam optimizer, and Dropout mechanisms, all balancing model complexity with generalization. Finally, performance differences between GCN and ResNet, Apriori, and FP-Growth were compared. [Results] Experimental results demonstrate that the proposed algorithm significantly outperforms traditional methods in both accuracy and runtime. Specifically, when the data volume reaches 6 000 entries, GCN achieves stable alarm accuracy exceeding 92％, surpassing Apriori (83％), FP-Growth (87％), and ResNet (84％—94％) with smaller fluctuations. In terms of efficiency, GCN′s average processing time is comparable to FP-Growth (with a difference of less than 5％ for data volumes exceeding 1 000 entries) and significantly lower than ResNet. Moreover, by capturing nonlinear correlations and hierarchical root causes in alarm data, GCN effectively suppresses the noise interference, validating its robustness in complex network environments. [Conclusions] The proposed GCN-based alarm compression algorithm achieves high-precision, low-redundancy alarm information extraction by deeply integrating spatiotemporal features and topological correlations of alarm data. Compared to traditional methods, it shows significant advantages in both accuracy and efficiency, providing reliable technical support for intelligent network O＆M. Future work will focus on lightweight model design and real-time optimization to further adapt to large-scale dynamic network scenarios.

[Objective] This paper addresses the consensus control problem of linear multi-agent systems with communication delays under an event-triggered mechanism to reduce communication frequency while ensuring system stability. Traditional consensus control schemes mostly rely on periodic information updates where frequent communication not only consumes resources but may also cause system instability. To address this issue, a distributed control method that integrated pinning control strategy with an integral-type event-triggered mechanism was proposed for multi-agent systems with communication delays, enabling consistent driving of agent states. [Methods] First, a virtual leader was introduced to transform the consensus problem into an asymptotic stabilization problem of the corresponding error system. Then, a feedback controller incorporating local agent states, neighboring information, and leader-guidance terms was designed. Combined with an integral triggering function, a triggering schedule was constructed to reduce communication updates. Subsequently, based on the constructed error-system model, a Lyapunov-Krasovskii functional was developed that accounted for the current state, communication-delay terms, double-integral terms, triggering-error terms, and delayed-derivative terms. Sufficient conditions were obtained to guarantee the global asymptotic stability of the closed-loop error system. On the basis of stability, the potential Zeno behavior induced by the event-triggered mechanism was further analyzed. By introducing an upper bound on the growth of the error energy and a dynamic constraint on the integral triggering function, a positive lower bound on the inter-event intervals was derived, and a set of sufficient conditions to exclude Zeno behavior were provided. [Results] Simulation results demonstrate that, even in the presence of communication delays, the proposed integral event-triggered mechanism achieves consensus while significantly reducing triggering frequency and control energy consumption achieving the lowest overall energy consumption. Moreover, the convergence curves become smoother and avoid excessive triggering caused by high-frequency small disturbances, demonstrating improved convergence stability and energy efficiency. [Conclusions] Compared with traditional and dynamic event-triggered schemes, the proposed control strategy achieves better overall performance in communication and energy utilization while guaranteeing consensus, demonstrating higher practical value for resource-constrained multi-agent systems. This study provides new theoretical support and a methodological framework for the distributed control of delayed multi-agent systems.

[Objective] In industrial control systems (ICS), communication between devices rely heavily on industrial control protocols, and the security of these protocols is essential for stable ICS operation. Vulnerability detection and intrusion detection, as core components of the ICS defense framework, require accurate analysis of protocol structures and semantic functions. Protocol reverse engineering serves as a key technique for this purpose, and the precision of semantic inference directly determines the accuracy of protocol understanding. However, due to the absence of protocol documentation and strong format heterogeneity, existing semantic inference methods generally rely on expert knowledge, resulting in insufficient automation and limited cross-protocol generalization. Consequently, they fail to meet the high precision analysis needs of multi-source heterogeneous protocols in real industrial environments. [Methods] To solve the above problem, this study proposed a semantic inference method that integrated mBERT, multi-source domain adaptation, and a structured masking strategy. Cross-protocol semantic representations were achieved through the mBERT model. A structured masking strategy that combined attention weights and positional encoding was designed to enhance the model′s ability to capture intrinsic correlations between protocol structure and semantics, which improved the automation and efficiency of semantic inference. A progressive multi-source domain adaptation strategy with adversarial training further strengthened the model′s generalized semantic representation across multiple source protocols, enhanced its applicability to various industrial control protocols, and enabled effective inference of keyword semantics. [Results] Experiments were conducted in the target range for offensive and defensive drills in typical energy enterprises in the Key Laboratory of Information Security for the Petrochemical Industry in Liaoning Province. Data from three industrial control protocols, S7comm, Modbus/TCP, and EtherNet/IP, were collected, and a training dataset was built using a protocol-complexity scoring mechanism. The results show that the progressive multi-source domain adaptation strategy significantly improves model performance. When it is combined with the structured masking strategy, semantic inference accuracy is further enhanced. The proposed method achieves significantly higher precision, recall, and F₁-score compared with existing baseline methods. [Conclusions] This study proposes a semantic inference method that integrates mBERT, multi-source domain adaptation, and structured masking. High-dimensional spherical mapping and multi-task loss functions used in semantic inference improve the distinguishability of different semantic categories and enhance the model′s deeper recognition capability for protocol semantics. The proposed method significantly reduces reliance on manual prior knowledge, increases inference efficiency, and improves cross-protocol applicability. It provides a theoretically grounded new pathway for industrial control protocol reverse engineering and ICS security protection.

[Objective] Given the widespread use of drone swarms in reconnaissance missions, optimizing the deployment of air defense countermeasure systems has become a critical issue for enhancing defensive capabilities. Drone swarms, with their high flexibility, robust survivability, and cost-effectiveness, pose a significant threat to traditional air defense frameworks. A single air defense system struggles to effectively address the coordinated multi-target nature of drone swarms, necessitating the collaborative deployment of multiple systems to maximize the flight cost of the swarm, thereby forcing path alterations or mission abandonment. This study aims to develop an efficient deployment method for air defense countermeasure systems to mitigate the security challenges posed by drone swarm reconnaissance. [Methods] This study introduced a deployment method for air defense countermeasure systems against drone swarms, based on water wave optimization (WWO) and the A^* algorithm, termed the water wave and A^* deployment (WAD) algorithm. The approach integrated two key sub-models:first, an optimal path planning model for drone swarms, which calculated the minimum flight cost under specified air defense countermeasure system positions; second, an air defense countermeasure system location optimization model that adjusted system positions to maximize the expected flight cost of the swarm. The WAD algorithm leveraged WWO′s balanced global and local search capabilities alongside the A^* algorithm′s efficiency in path planning, enhanced by an improved encoding-decoding scheme to boost search efficiency and avoid suboptimal solution spaces. [Results] The effectiveness of the WAD algorithm is confirmed through simulation experiments. The experimental scenario includes 4 flight starting points, 39 waypoints, and 3 air defense countermeasure systems. Results demonstrate that the WAD algorithm is able to obtain the maximum expected flight cost for the drone swarm and output optimized deployment positions for the air defense countermeasure systems and the swarm′s flight paths. The population′s best fitness converges rapidly with the increase of iteration counts, stabilizing within an average of 30 iterations, highlighting the algorithm′s high precision and computational efficiency, and significantly shortening the optimization time compared with traditional methods. [Conclusions] The WAD algorithm provides an efficient solution for optimizing the deployment of air defense countermeasure systems against drone swarms. By integrating the strengths of WWO and the A^* algorithm, it achieves an effective balance between global exploration and local exploitation, markedly improving convergence speed and optimization quality. The findings indicate that this method is applicable to defense requirements in complex reconnaissance scenarios. Future work may further investigates multi-objective optimization strategies in dynamic environments, explores coordination mechanisms among air defense countermeasure systems, and incorporates real-time threat assessment to adapt to the rapid evolution of drone swarm technologies.

[Objective] Due to small scale and large specific surface area, nanomaterials have attracted increasing attention in the field of repair materials for earthen sites. Given the severe degradation of earthen sites in Liaoning that urgently require restoration, this study focuses on nano-titanium dioxide (TiO₂) and graphene oxide (GO), and investigates how TiO₂ and GO affect the unconfined compressive strength (UCS), triaxial shear properties, and microstructures of soil repair materials. It further clarifies the synergistic enhancement mechanism of the two nanomaterials. [Methods] Silicon-composite modified original soil (S-GLS) was used to prepare nano-TiO₂-GO-modified soil with different TiO₂ mass fractions (i.e., 1％, 3％ and 5％) and different GO mass fractions (i.e., 0.02％, 0.025％, and 0.03％), and the physical properties of nano-TiO₂-GO modified soil (SN-GLS) were studied. The mechanical behavior and full stress-strain curves of SN-GLS were investigated by UCS and triaxial shear tests. The microscopic morphology, pore characteristics, and pore-size distribution of SN-GLS were characterized by scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) tests. [Results] With the addition of TiO₂-GO, the compressive strength and shear strength of SN-GLS are significantly improved. Triaxial shear tests reveal a transition in the macroscopic failure mode of the specimen from oblique shear failure to a complex form that includes both swelling and multiple shear failures. The transition is characterized by lateral swelling in the central region of the specimen and the formation of cracks consisting of various shear bands, including vertical and transverse orientations. With 3％ nano-TiO₂ and 0.025％ GO at 28 days, the strain of SN-GLS peaks at the range from 2.5％ to 4％, and the residual strength retention rate is from 75％ to 90％. The incorporation of nano-TiO₂-GO increases the ductility of the original soil. SN-GLS has superior shear strength and deformation characteristics. Compared with S-GLS, cohesion increases by 61.8％ and 42.7％ at 7 and 28 days, respectively. The internal friction angle at 28 day increases by 1°. SEM and MIP results indicate that, compared with S-GLS, the optimization of microstructures and the enhancement of mechanical properties in SN-GLS arise from the synergistic action between GO and TiO₂. Specifically, the incorporation of GO produces a rough and wrinkled layered structure that increases the interlayer spacing of the original soil. Its oxygen-containing functional groups provide nucleation sites for hydration reactions and act as a “template”, thus promoting the formation of more hydration products (e.g., C-S-H) and markedly improving the mechanical properties. Nano-TiO₂ contributes through its nanoscale effect and filling effect, filling internal pores, strengthening the bonding between soil particles, and altering the arrangement of the soil skeleton to improve its orientation, which consequently increases the fractal dimension of SN-GLS by 0.045 9. These combined effects significantly optimize the pore-size distribution of SN-GLS, with the proportion of medium pores decreased by 18％, small pores increased by 16％, and micropores increased by 2％, indicating a transformation of medium pores into small and micropores, a denser microstructure, and an overall reduction of total porosity by 10.09％. [Conclusions] This study provides a new idea for the research and improvement of repair materials used in earthen sites. Nano-TiO₂ and GO have potential application value for the protection and reinforcement of earthen sites.

[Objective] With the gradual achievement of independent research and development of gas turbine engines, the research and development of turbine blades, known as the “jewel in the crown”, have attracted much attention. A part of the gas turbine blades are hollow in structure, and the ceramic cores are the main components that form the hollow blade cavity structure. Due to the large size of the hollow blade, the high-temperature casting time is much longer, and this leads to an extension of the high-temperature holding time of the ceramic core. To ensure that the ceramic core meets the casting requirements of large-sized hollow blades, it is particularly important to consider the long-term high-temperature creep behavior of the ceramic core. Therefore, the effect of alumina content on the long-term high-temperature creep behavior of silica-based ceramic cores was investigated in this paper. [Methods] First, fused silica was selected as the matrix, and the silica-based ceramic cores with different alumina contents were prepared. After sintering at 1 200 ℃, a creep test was conducted at 1 500 ℃ for 1 h using the suspension method, during which the creep deformation characteristics were observed and the deformation amount was measured. Scanning electron microscopy (SEM) was used to observe the fracture microstructures of the ceramic cores after creep at high temperature. X-ray diffraction (XRD) was used to analyze the phase compositions of the ceramic cores in different states. [Results] It is indicated that for the samples with 20％ alumina, the least porosity and the highest relative cristobalite content were detected after sintering and subsequent 1 500 ℃ for 1 h heat treatment. When the alumina content is 10％, the lowest creep deformation amount is obtained. The reaction between alumina and silica at high-temperature promotes viscous flow sintering, which increases the creep deformation of the ceramic cores. [Conclusions] Adding alumina can promote the phase transformation from fused silica to cristobalite during the sintering process. After high-temperature holding, as the content of alumina increases, alumina participates in the reaction, forming the mullite phase, causing viscous flow sintering and increasing the creep deformation amounts of the ceramic cores. To obtain silica-based ceramic cores with high strength and excellent long-term creep properties, it is necessary to reasonably control the content of alumina.

[Objective] As the requirements for operational precision and stability of rotating mechanical structures continue to increase, the dynamic performance of service bearings within the structures faces increasingly strict requirements. For enhancing the operational performance of rotating mechanical structures, it is crucial to reveal the change rules of dynamic contact characteristics of bearings under complex loading conditions. [Methods] Based on the classic Jones-Harris model and raceway control theory, a five-degree-of-freedom (5-DOF) dynamic contact characteristic analysis model was established by geometric and force analyses of components. The model systematically considered the effects of centrifugal force and gyroscopic moment and incorporated them into the analysis of bearing ball-raceway contact and separation states to comprehensively describe the dynamic contact characteristics of ball bearings under axial, radial, and moment loads and rotional speeds. In addition, a Newton-Raphson iterative algorithm combining inner and outer loops was established to solve local and global equations. The model was simplified according to the internal geometric and force relationships of bearings to solve variables so that its computational complexity could be effectively reduced. With the NSK 7013C bearing as an example, the change rules of dynamic contact characteristics of the bearing under specific load conditions were analyzed. [Results] Under a small axial load and a certain rotional speed, incomplete contact state occurs between the balls and raceways inside the bearing, with the incomplete contact region positively correlated with the rotional speed. Moreover, increasing the axial load appropriately reduces the variation in contact load amplitudes on the inner and outer races and eliminates the incomplete contact state. Only increasing the radial load leads to incomplete contact inside the bearing, but the incomplete contact region stops growing once the radial load reaches a certain level. Under the same load conditions, the contact angles and contact loads on the inner and outer races of the bearing vary with the change of azimuthal angle of the rolling elements. Radial and moment loads in different directions cause fluctuations in contact angles and loads, with their effects overlapping. [Conclusions] Axial loads can improve the load distribution within the bearing and suppress individual bearing ball separation. Axial, radial, and moment loads and rotational speed have varying degrees of impact on the dynamic contact characteristics of the bearing, with the moment load having the less influence. The innovation of this study lies in systematically considering the effects of external loads and the full working states of bearing ball-raceway contact and separation. It analyzes the change rules of dynamic contact characteristics under complex load conditions, providing new theoretical support for bearing selection, forward design, and operational performance improvement of rotating machinery.

[Objective] There are still shortcomings in the transfer learning of gear fault diagnosis between different devices in existing research, especially under small sample conditions, and the diagnostic accuracy still needs to be improved. Therefore, a gear fault diagnosis method for small samples was proposed in this paper, which combined self-calibrated convolution integrated split attention network SCResNeSt50 and transfer learning strategy. [Methods] Firstly, the continuous wavelet transform was used to perform time-frequency analysis on the gear signal, generating a time-frequency graph as the model input. Secondly, based on the residual split attention network (ResNeSt) structure, a split attention mechanism and self-calibrated convolution were integrated to improve the linear processing mode of traditional convolutional neural networks for time-frequency graphs. The self-calibrated convolution was used to replace the conventional convolution in the ResNeSt module to achieve adaptive response calibration and multi-scale feature encoding, thereby expanding the receptive field and enhancing the ability to characterize fault features. Finally, a transfer learning strategy was adopted to fine tune the classifier parameters of the pre-trained model in the source domain, and the feature extraction layer was frozen, in order to achieve effective adaptation of the target task while preserving the general knowledge and feature representation of the source model and improving the accuracy of gear fault diagnosis under small sample conditions. [Results] Experiments were conducted on the gearbox dataset of Southeast University and the gear dataset of the University of Connecticut to verify the effectiveness of the method. The experiment included two scenarios: transfer learning under variable operating conditions and cross-dataset transfer learning. The proposed method was compared and analyzed with existing fault diagnosis methods. The results show that in the experiment of transfer learning under variable operating conditions, the diagnostic accuracy of the target domain reaches 98.7% and 98.9%. In the migration experiment from the dataset of Southeast University to that of the University of Connecticut, when the sample size of each gear state in the target domain training set is 25, 20, 16, 12, 8, and 6, the diagnostic accuracy reaches 98.1%, 98.1%, 97.8%, 97.5%, 96.5%, and 93.1%, respectively. [Conclusions] This method achieves better diagnostic accuracy than other methods in multiple experiments, which indicates that the improved self-calibrated convolution effectively enhances the characterization ability of gear fault features, and the transfer learning strategy significantly enhances the reliability of fault diagnosis under small sample conditions. This study provides a feasible solution to gear fault diagnosis under small sample conditions, promoting the development of intelligent fault diagnosis technology.

[Objective] The stress state of steel components is a key parameter for assessing the safety and health of steel structures during long-term service. Existing stress detection methods based on the magnetically induced electromotive force effect do not consider the influence of the magnetic field direction, and variations in the magnetic field angles reduce the accuracy of detection results. Therefore, this study quantitatively investigates the influence of the magnetic field angle on the relationship between stress and magnetically induced electromotive force in steel structural columns through theoretical analysis and experimental research. [Methods] Axial compression tests on short Q235B H-shaped steel column specimens were designed. A manganese-zinc ferrite U-shaped electromagnetic sensor was fixed at the center of the steel flange, and its orientation was varied. During the graded loading process, magnetically induced electromotive force signals at different stress levels and magnetic field angles were collected simultaneously. All tests were repeated twice to ensure data reproducibility. Based on the experimental data, magnetically induced electromotive force-stress relationship curves at different magnetic field angles were established. The specific influence of magnetic field angle on this relationship was examined, and the quantitative relationship among magnetically induced electromotive force, stress, and magnetic field angle was clarified. In addition, the mean gradient of the magnetically induced electromotive force was used to quantify the effect of magnetic field angle on the electromotive force-stress relationship. [Results] Within the elastic range, the magnetically induced electromotive force shows a significant linear negative correlation with compressive stress. The slope of the relationship curve changes markedly with increasing magnetic field angle. The signal sensitivity is the highest when the magnetic field direction is parallel to the principal compressive stress direction. As the magnetic field angle increases, the sensitivity decreases nonlinearly and reaches its minimum value when the magnetic field direction is perpendicular to the stress direction. The characteristic curves corresponding to different angles do not intersect. Under different magnetic field angles, the gradient of the magnetically induced electromotive force tends toward a constant value with increasing stress, and its mean value decreases as the deviation angle between the magnetic field direction and the loading direction increases. [Conclusions] The magnetic field angle affects the accuracy of stress detection. The mathematical expression for the relationship between magnetically induced electromotive force and stress established based on the experimental results shows a good fit. Furthermore, the mean gradient of the magnetically induced electromotive force follows a cosine-function relationship with the magnetic field angle.

[Objective] In the actual construction of road engineering, it is often difficult for high-salt soft soil to meet the technical requirements of engineering construction directly due to its special physical and mechanical properties. Therefore, a systematic study on the solidification characteristics of high-salt soft soil should be conducted to provide theoretical support and technical references for the reasonable utilization of this type of soft soil in road engineering via an in-depth analysis of various characteristics presented during soft soil solidification. [Methods] Laboratory experiments were combined with theoretical analysis to conduct indoor solidification of high-salt soft soil, and the strength characteristics and micro-mechanism of the solidified soft soil were studied. Meanwhile, the cement single-mixing tests and the three-factor, three-level orthogonal tests of cement, lime and fly ash were set up, respectively. By focusing on the two types of tests, the effects of salt contents of 1.5％, 3.5％, 5.5％, and 7.5％ on the unconfined compression strength of high-salt solidified soft soil were explored. Then, the X-ray diffractometer (XRD) and scanning electron microscope (SEM) were adopted to further investigate the micro-mechanism of the solidified high-salt soft soil, and thus gain a deeper understanding of the mechanism. [Results] The cement single-mixing test shows that under the constant initial water content and salt content of soft soil, there is optimal mixing content for cement solidification of high-salt soft soil. Additionally, by setting different mixing contents of cement, lime, and fly ash for composite solidification treatment of soft soil with salt content of 7.5％, the maximum unconfined compression strength of the samples after 7, 14, and 28 days of curing can reach 326 kPa, 388 kPa, and 593 kPa, respectively, which can better meet the construction needs of actual projects. [Conclusions] When the combination of 7％ cement+3％ fly ash+3％ lime is employed as the inorganic gelling solidification agent, the relatively ideal solidification effect can be achieved for soft soil with different salt contents. It should be noted that under the premise of keeping the mix ratio of the solidification agent unchanged, the strength of the solidified soft soil shows a trend of continuous deterioration with the gradually increasing salt content in the soft soil. Additionally, the effect of chlorine salt on the strength of solidified soft soil becomes increasingly significant with the rising curing age.