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    Supply Chain Resilience: Research Review and Prospects
    Zhong-zhong JIANG, Jia-run GUO, Wei ZHENG
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 59-70.   DOI: 10.12068/j.issn.1005-3026.2025.20250055
    Abstract1407)   HTML42)    PDF(pc) (2764KB)(1126)       Save

    In recent years, compounded crises such as geopolitical conflicts (e.g., the Russia-Ukraine conflict) and technological containment (e.g., the China-U.S. trade friction) have continuously exerted a profound impact worldwide, revealing the vulnerabilities of global supply chains. Enhancing the supply chain resilience has become a critical strategy to ensure the sustainable development of countries around the world, especially China, and it serves as a vital foundation for making China strong in manufacturing. On this basis, existing research on supply chain resilience was comprehensively reviewed, with particular focus on its origins, conceptual definitions, and driving factors. The evolution of the research was systematically analyzed, and prospective research directions were explored from four dimensions: collaborative optimization, resource allocation, dynamic response, and risk management. The findings aim to provide theoretical support and decision reference for enhancing supply chain resilience both globally and within China.

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    Development and Prospects for Software‑Defined Intelligent Control Systems
    Tian-you CHAI, Rui ZHENG, Yao JIA, Xin-yu HUANG, Yan-jie SONG
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 1-10.   DOI: 10.12068/j.issn.1005-3026.2025.20250079
    Abstract1187)   HTML84)    PDF(pc) (4009KB)(588)       Save

    The current state of research on software-defined control systems was reviewed, and the role and development of control systems throughout the industrial revolutions were analyzed. The intelligent development direction for software-defined control systems was proposed. The case study of a software-defined end-edge-cloud collaborative PID(proportional-integral-derivative) tuning intelligence system was presented, which demonstrates that the tight conjoining and coordination between industrial artificial intelligence, industrial Internet, and other new-generation information technologies with software‐defined control systems has opened up a new way for the development of software-defined intelligent control systems. Finally, the principal research directions for software-defined intelligent control systems were pointed out by considering the challenges faced by software-defined control systems and those specific to their intelligent transformation.

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    Absolute Position Accuracy Calibration Algorithm for Robots Based on Joint Geometric Error
    Liang LIANG, Cheng-dong WU, Shi-chang LIU
    Journal of Northeastern University(Natural Science)    2025, 46 (4): 1-7.   DOI: 10.12068/j.issn.1005-3026.2025.20230303
    Abstract1159)   HTML116)    PDF(pc) (1793KB)(700)       Save

    An industrial robot kinematic model with joint geometric error parameters and a calibration algorithm is proposed. Firstly, based on the DH model, six geometric error parameters are introduced for each joint to establish a more comprehensive error calibration model. The solutions of forward and inverse kinematic for the model are realized. Then, a differential kinematic Jacobian matrix containing 45 parameters, including joint errors, base coordinate errors, and tool coordinate errors is established. An iterative algorithm based on a small sample test set is used to solve the error parameters. Finally, experimental verification is carried out using a laser tracker on the SIASUN SR10C robot. The calibrated error parameters are then compensated into the model. Results show that, after calibration compensation, the maximum position error of the robot decreases by approximately 80%, the average position error decreases by approximately 80%, and the error variance decreases by approximately 97%, demonstrating that this method can significantly improve the absolute position accuracy and determinacy of industrial robots.

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    Research Progress on the Corrosion Failure Behavior of Coatings on Aluminum Alloy for Semiconductor Fabrication Equipment
    Yang ZHAO, Yu-hang WANG, Tao ZHANG, Fu-hui WANG
    Journal of Northeastern University(Natural Science)    2025, 46 (3): 28-45.   DOI: 10.12068/j.issn.1005-3026.2025.20240182
    Abstract936)   HTML33)    PDF(pc) (5616KB)(1852)       Save

    In the semiconductor fabrication equipment, the coatings on aluminum alloy often fail due to the coupling effect of high-temperature, vacuum and aggressive gases, including their plasma. In the chlorine-based plasma, the anodized coating has a high etching rate, leading to rapid removal, while the etching rate of Y2O3 coatings is approximately one in 50 of that of the anodized coating. In the fluorine-based plasma, both the anodized coating and Y2O3 coatings experience particle contamination due to the fluoride layer peeling. The corrosion resistance of the anodized coating can be significantly enhanced by adjusting the composition and temperature of the electrolyte or depositing a pure aluminum layer on the aluminum alloy surface. Additionally, improving the density of Y2O3 coatings can reduce their etching rate. Combining these strategies with remote plasma cleaning techniques can minimize the impact of charged particles on chamber materials, significantly reducing particle contamination in the reaction chamber. During the etching and thin film deposition processes, changes in the chamber surface composition can alter the plasma state, leading to various process defects.

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    Review of Multi-type Energy Routers Research
    Qiu-ye SUN, Rong-da XING, Qian-xiang SHEN, Zhen-ao SUN
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 11-21.   DOI: 10.12068/j.issn.1005-3026.2025.20250063
    Abstract920)   HTML16)    PDF(pc) (1261KB)(326)       Save

    Energy routers (ERs) are one of the core components of the energy Internet for achieving multi-port energy conversion and active energy flow control. This paper classified ERs into three categories: electrical ERs, information ERs, and multi-energy ERs. Based on the differences between these categories, the research on ERs is divided into four aspects: electrical conversion, focusing on topology and control of multi-port electrical conversion; energy routing control, primarily concerned with the regulation of power flow between ports of ERs; information processing and optimal control, emphasizing the acquisition and transmission of information and optimizing energy flow; and multi-energy coordination, with multi-energy comprehensive utilization as the main goal. Based on these four research aspects, this paper explored topology, control, communication, and multi-energy optimization of ERs, as well as the interrelationships between different aspects.

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    Active Obstacle Avoidance Path Planning for Multi-scenario Autonomous Vehicles Under Icy and Snowy Road Conditions
    Yu-long PEI, Shuang-zhu ZHAI
    Journal of Northeastern University(Natural Science)    2025, 46 (3): 1-11.   DOI: 10.12068/j.issn.1005-3026.2025.20239039
    Abstract886)   HTML64)    PDF(pc) (4698KB)(1084)       Save

    Addressing the issue of autonomous vehicles’ instability on icy and snowy roads, an improved rapidly-exploring random tree (RRT) path planning algorithm is proposed. Firstly, a dynamic model introducing road adhesion coefficient on icy and snowy roads is established. Secondly, the global target deflection sampling combined with the front pointing and steering angle of the vehicle, combined with the collision avoidance detection and the maximum curvature constraint under the velocity-adhesion coefficient, is used to improve the traditional RRT algorithm problem.Finally, a double quintic polynomial is used for path smoothing to ensure stability, brake constraints, and comfort. The performance of the improved algorithm RRT is compared with that of the traditional algorithm under multi-scenario conditions through the joint simulation of MATLAB-Simulink and CarSim. The experiments show that the improved RRT algorithm significantly improves the path smoothness, reduces the curvature mutation, has short time, high success rate and good stability when driving on ice and snow.

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    Intelligent Identification Method of Industrial Mixed Gases Based on ConvGRU Fusion Attention Mechanism
    Fan-li MENG, Shu-chang LI, Hao WANG, Zhen-yu YUAN
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 37-48.   DOI: 10.12068/j.issn.1005-3026.2025.20240164
    Abstract870)   HTML20)    PDF(pc) (4835KB)(221)       Save

    To address the issue of high data dependency and insufficient accuracy in mixed gas identification for traditional semiconductor gas sensors, a ConvGRUAttention network model that integrates gated recurrent units (GRU), convolutional layers, and attention mechanism is proposed. Empirical wavelet transform (EWT) is employed to convert raw signals into the time-frequency domain and perform multi-scale decomposition, which suppresses noise, reduces data dependency, and enhances the model’s robustness. The model extracts local dynamic features through convolutional layers, captures long-term dependencies using GRU, and optimizes feature weights across multi-scale signals via the attention mechanism, thereby improving feature extraction and generalization capabilities. Experimental results demonstrate 100% accuracy in qualitative identification and a root mean square error (RMSE) of 3.3×10⁻⁶ in quantitative detection. Compared with the traditional methods, the detection accuracy for mixed gases is significantly improved.

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    Application of Improved PSO-PH-RRT* Algorithm in Intelligent Vehicle Path Planning
    Qi-long JIANG, Jian XU
    Journal of Northeastern University(Natural Science)    2025, 46 (3): 12-19.   DOI: 10.12068/j.issn.1005-3026.2025.20239047
    Abstract814)   HTML32)    PDF(pc) (2392KB)(287)       Save

    In application scenarios like robot control and autonomous navigation of intelligent vehicle, path planning needs to account for factors including obstacles and terrain. To address the issues of directionless expansion target and low efficiency in rapidly-exploring random tree (RRT) algorithm in path planning, a particle swarm optimization for probabilistically homogeneous rapidly-exploring random tree (PSO-PH-RRT*) algorithm is proposed. This algorithm base on the probabilistically homogeneous rapidly-exploring random tree (PH-RRT*) algorithm by using the particle swarm optimization algorithm to update the probability of direction as the velocity direction for random tree nodes, thereby improving the node position update strategy. It also uses the distance between the node and the target vector, along with trajectory smoothness, as the fitness function in the particle swarm optimization algorithm. Finally, simulations across various scenarios demonstrate that the PSO-PH-RRT* algorithm can significantly reduce iteration time costs while improving path length and smoothness.

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    Reliability Optimization of Process Parameters Considering Milling Surface Morphology
    Xian-zhen HUANG, Xu WANG, Peng-fei DING, Zhi-yuan JIANG
    Journal of Northeastern University(Natural Science)    2025, 46 (3): 80-87.   DOI: 10.12068/j.issn.1005-3026.2025.20230275
    Abstract806)   HTML17)    PDF(pc) (2578KB)(428)       Save

    Research is conducted on the ball end milling process with the aim of achieving reliable optimization of milling process parameters. Firstly, according to the motion trajectory of the ball end mill cutting edge, the surface morphology formed during machining is simulated using the Z-mapping (Z-MAP) algorithm, and the surface roughness (Ra) is introduced to measure the surface quality after machining. The accuracy of the surface morphology simulation model is validated through surface morphology analysis experiments. Then, considering the actual constraint conditions of the machining surface quality, the tool service life, and the uncertainty of process parameters during the machining process, a reliability optimization model for process parameters is established with spindle rotation speed, tool feed rate, axial cutting depth and radial cutting depth as the optimization variables, and maximizing the material removal rate (Q) as the optimization objective. Finally, the optimization model is solved using the grey wolf optimization algorithm to obtain the optimal process parameters, and the feasibility of the optimized results is verified through milling experiments.

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    Research on Localization of Industrial Intelligent Inspection Robots in Cable Tunnel Environment
    Yu-tao WANG, Jun-wei AN, Chang-sheng QIN, Wei-fan GUO
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 49-58.   DOI: 10.12068/j.issn.1005-3026.2025.20240212
    Abstract778)   HTML13)    PDF(pc) (5943KB)(87)       Save

    The cable tunnel is closed and narrow, with repetitively laid cable racks and similar scene textures, which is a degraded scenario. To address this environment, a visual-inertial SLAM (simultaneous localization and mapping) algorithm based on point-line feature fusion is proposed. The algorithm improves the high-dimensional line features through length suppression and short line fitting to make it more effective in describing the structural features of tunnel scene. In addition, for the problem of loop closure detection failure due to feature similarity in cable tunnels, ArUco markers with efficient recognition and accurate pose estimation are introduced to limit the loop closure area, and the optimal loop closure frames are selected using the minimized pose transformation to improve detection accuracy and localization precision. Finally, dataset collection and experimental validation were conducted in actual cable tunnels. The results show that the absolute trajectory accuracy of the algorithm is improved by 69.73% on average relative to VINSMono(visual intertial system-Mono), which meets the application requirements of cable tunnel inspection.

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    Explosion Characteristics of NCM Lithium-Ion Battery Vent Gases After Thermal Runaway Under High Temperature Conditions
    Gang LI, Xiu-peng ZHANG, Wei-da CHANG, Wei ZHOU
    Journal of Northeastern University(Natural Science)    2025, 46 (4): 78-86.   DOI: 10.12068/j.issn.1005-3026.2025.20230268
    Abstract724)   HTML4)    PDF(pc) (1911KB)(493)       Save

    In order to evaluate the risk of deflagration in high temperature environments caused by NCM lithium-ion battery vent gas (BVG) after thermal runaway, the explosion characteristics and laminar burning velocity of BVG at different initial temperature θ0 (25~120 ℃) were tested using an 8 L explosive chamber and a Bunsen burner. At the same time, the influence mechanisms of laminar burning velocity(SL) at room temperature and high temperatures were further analyzed by CHEMKIN numerical simulations. The results show that the LFL doesn’t change significantly with the increase of the initial temperature, and UFL increases. When θ0 increases to 120 °C, pmax decreases from 0.62 MPa to 0.45 MPa, and the relationship with θ0 is exponential. Affected by both positive and negative effects, (dp/dtmax decreases to different degrees with the increase of θ0; LOC decreases exponentially from 7.39% to 7.03%; SL increases with the increase of θ0. It is also found that C2H4 and H2 are the decisive factors affecting the combustion and explosion damage degree of BVG. The research results can provide a reference for the risk assessment and prevention of environmental deflagration caused by thermal runaway in NCM lithium-ion batteries.

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    Temperature Field Analysis and Machining Modeling of Inconel 718 for Wire Electrical Discharge Machining
    Yao-man ZHANG, Shuang-jin WU, Zhao-feng RAO
    Journal of Northeastern University(Natural Science)    2025, 46 (3): 88-96.   DOI: 10.12068/j.issn.1005-3026.2025.20230278
    Abstract712)   HTML10)    PDF(pc) (7759KB)(422)       Save

    Aiming at the characteristics of Inconel 718 material such as high work hardening rate and large cutting temperature variation, the machining mechanism and modeling of Inconel 718 were deeply studied by taking the discharge machining process of wire electrical discharge machining as the research object. The temperature field of single-pulse discharge is analyzed by the finite difference method and finite element simulation, and the theoretical and simulation temperature distribution results under given parameters are obtained. Furthermore, the law of the influence of pulse width on the size and shape of the corrosion pit is further explored. On the basis of considering the influence of the recast layer on the size of the pit, the surface roughness and material removal rate of machining are predicted and compared with the experimental data. The results show that with the change of pulse width, the variation trend of the theoretical and simulated electric pit dimensions is consistent. The maximum error between theoretical and simulation data and experimental results is 9.88%.

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    Fault Feature Extraction and Analysis of Rotating Blade Cracks
    Hong GUAN, Qian XIONG, Hui MA, Wei-wei WANG
    Journal of Northeastern University(Natural Science)    2025, 46 (3): 60-68.   DOI: 10.12068/j.issn.1005-3026.2025.20230267
    Abstract709)   HTML8)    PDF(pc) (1676KB)(212)       Save

    To complete the fault characteristic extraction of blade cracks, a finite element model of a cracked blade incorporating breathing effect was first established based on the Mindlin-Reissner shell element. The dynamic response of the cracked blade under the combined action of centrifugal and aerodynamic loads was solved, providing excitation signal input for fault feature extraction. Then, fault indicators based on the nonlinear output frequency response function and energy indicators were established. Finally, the effectiveness of various indicators in extracting the fault characteristics of rotating blade cracks was analyzed. The results showed that the contribution rate indicator Fen) and weighted contribution rate indicator Rnn) are unstable and insensitive in diagnosing blade crack faults, whereas the energy indicator effectively extracts blade crack fault characteristics under both resonant and non-resonant state. These results provide engineering guidance for feature extraction, analysis, and indicator selection of rotating blade crack faults.

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    Effect of TiAlSiN Coating Structure on Its Mechanical Properties
    Xing-long LIU, Chen LI, Zeng LIN
    Journal of Northeastern University(Natural Science)    2025, 46 (4): 33-42.   DOI: 10.12068/j.issn.1005-3026.2025.20230286
    Abstract703)   HTML14)    PDF(pc) (4777KB)(613)       Save

    TiSi (atomic ratio 80∶20) and AlTi (atomic ratio 67∶33) alloys were used as target materials by the vacuum arc ion plating technique. Two layers and multiple layers of TiAlSiN coating were deposited on the WC-Co substrates to study the effects of the coating structure on the microstructure, mechanical properties, and tribological properties of the coatings. TEM,SEM, EDS, XRD, nano-indentation instrument, microhardness instrument and binding force tester were used to analyze the cross sections of the coatings and the morphologies, compositions, microstructures, elastic moduli, microhardness and binding force of the coating. The tribological properties of the coatings were analyzed by the friction and wear testing machine. The results showed that the binding force (greater than 200 N) of the multilayer coatings is higher than that of the double-layer coatings. The double-layer coatings exhibit stronger resistance to plastic deformation, while the multilayer coatings show stronger resistance to elastic deformation. The friction coefficient of the coatings under low loads is greatly affected by the surface topography of the coating, while the surface topography of the coating under large load has little effect on the friction coefficient. Oxidation wear occurs only in the double-layer coatings, while abrasive wear occurs in the friction wear process of both coatings. The wear resistance of the multi-layer coatings is higher than that of the double-layer coatings.

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    Digital Twin Fault Diagnosis Method of Power Transformer Based on Industrial Intelligence
    Jian FENG, Bo-wen ZHANG, Ning ZHAO, Hui-jie JIANG
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 22-29.   DOI: 10.12068/j.issn.1005-3026.2025.20240218
    Abstract703)   HTML17)    PDF(pc) (4393KB)(157)       Save

    As a key development direction integrating new-generation information technology with advanced manufacturing techniques, industrial intelligence leverages intelligent, digital, and automated methods to significantly enhance industrial production efficiency and optimize the prediction and maintenance management of industrial equipment. This paper focuses on the intelligentization of industrial equipment, with the goal of ensuring the efficient and stable operation of power transformers within power systems. A digital twin model for transformer inter-turn short circuit faults is constructed based on electromagnetic field equations and equivalent circuit models. The model analyzes the symmetry of the transformer in both normal and fault conditions from an electromagnetic field perspective, thereby integrating digital twin technology with fault diagnosis. Furthermore, through in-depth analysis of the virtual model of the transformer, the location of faults is accurately identified, ensuring the safe operation of the transformer, improving its reliability and efficiency, and advancing the intelligentization and modernization of the entire power system.

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    Prediction Model of Burning Through Point Based on JITL-XGBoost
    Jin-yang WANG, Zhao-xia WU, Zhong-zheng LI, Zeng-xin KANG
    Journal of Northeastern University(Natural Science)    2025, 46 (2): 28-34.   DOI: 10.12068/j.issn.1005-3026.2025.20230256
    Abstract680)   HTML4)    PDF(pc) (1756KB)(557)       Save

    The burning through point (BTP) is an important parameter in the sintering process, which directly affects the efficiency of the sintering machine. Due to the multi-working conditions and time-varying characteristics of the sintering production process, the prediction performance of the global model is insufficient. Therefore, a burning through point prediction model using XGBoost as a local model in the just-in-time learning framework is proposed, namely JITL-XGBoost. Firstly, the KL divergence similarity measurement method is used to extract the characteristics of the sample to be tested, and the most relevant data set of the sample to be tested is selected. Secondly, this dataset is used as input to the XGBoost model to predict the location of the burning through point. In addition, the impact of related dataset numbers on model prediction accuracy and model computation time is considered. Finally, by comparing with other models, the results show that the model built has the best prediction accuracy within a reasonable time, providing new guidance for improving the efficiency of sintering machines.

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    Evaluation and Optimization of Green Development Efficiency of Construction Industry in China
    Xi-jing QI, Meng-xing ZHANG, Sheng-jin ZHANG
    Journal of Northeastern University(Natural Science)    2025, 46 (4): 115-123.   DOI: 10.12068/j.issn.1005-3026.2025.20230287
    Abstract679)   HTML6)    PDF(pc) (1523KB)(180)       Save

    An evaluation index system was established based on the green development and its efficiency in the construction industry.The static and dynamic efficiencies of green development in the construction industry at the national and provincial levels were estimated using the super efficiency SBM-ML model from 2008 to 2021. The results demonstrate that the static efficiency of green development in the construction industry in China is fluctuant rising. The efficiency value is greater than 1 for nine years. This implies that the green development in the construction industry is relatively effective. The green development efficiency in the construction industry has increased by an average of 1.7% annually, but the stability of dynamic efficiency in different years needs to be improved. The static efficiency of green development in the construction industry in each province shows a distribution of "high in the southeast and low in the northwest", and the efficiency value in each province shows an obvious differentiation of "high, medium and low". While there are fluctuations in the dynamic efficiency levels of green development in the construction industry in each province, there is no obvious manifestation of specific geographical distribution patterns. Through analyzing the calculation results, relevant suggestions are put forward from the aspects of policies and enterprises to optimize the green development efficiency and promote the high-quality development in the construction industry.

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    Electric Vehicle Charging Scheduling Strategy Based on Safe Reinforcement Learning Algorithm
    Heng-xin PAN, Run-da JIA, Shu-lei ZHANG
    Journal of Northeastern University(Natural Science)    2025, 46 (5): 1-9.   DOI: 10.12068/j.issn.1005-3026.2025.20230183
    Abstract675)   HTML35)    PDF(pc) (1237KB)(673)       Save

    As the number of electric vehicles (EVs) increases, reinforcement learning (RL) in EV charging scheduling faces challenges, particularly uncertainties and the curse of dimensionality from large‑scale applications. A microgrid model for residential areas, considering the vehicle‑to‑grid (V2G) mode and various nonlinear charging models is developed. The problem is formulated as a constrained Markov decision process (CMDP), with a model‑free RL framework to handle uncertainties. To address the curse of dimensionality, a strategy is designed where EVs are grouped by states, and agents send control signals to these sets, thus reducing the dimensionality of the action space. A Lagrangian deep deterministic policy gradient (LDDPG) algorithm is employed to solve the charging scheduling problem, with a safety filter ensuring constraint compliance. Numerical simulations validate the strategy’s effectiveness.

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    Impact of Paternalistic Leadership on Employees’ Work Behaviors: Based on Latent Profile Analysis Method
    Lan-xia ZHANG, Yong-xin YANG, Song-yan ZHAO
    Journal of Northeastern University(Natural Science)    2025, 46 (3): 145-152.   DOI: 10.12068/j.issn.1005-3026.2025.20230142
    Abstract664)   HTML9)    PDF(pc) (735KB)(587)       Save

    By using the latent profile analysis method, based on 564 questionnaire survey data, the types of paternalistic leadership in enterprises were divided, and further exploration was conducted on the impact of different types of paternalistic leadership on differences in employee work behavior, including both in-role behaviors and extra-role behaviors. It was shown that the paternalistic leadership in enterprises can be divided into four types, which are named high authoritarian leadership, enlightened leadership, low authoritarian leadership and balanced leadership. Among them, the level of employees’ in-role behaviors and extra-role behaviors under the influence of enlightened leadership is the highest, followed by the low authoritarian leadership. There is no significant difference in the level of employees’ in-role behaviors under the balanced leadership and high authoritarian leadership is higher. However, the level of employees’ extra-role behaviors under the balanced leadership. This study not only reveals the heterogeneity of paternalistic leadership, enriches the theoretical system of paternalistic leadership, but also provides the beneficial guidance for enterprises management practices.

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    Fault Diagnosis Method for Rolling Bearings Based on WP-TRP
    Na WANG, Yue-lei CUI, Liang LUO, Zi-cong WANG
    Journal of Northeastern University(Natural Science)    2025, 46 (3): 20-27.   DOI: 10.12068/j.issn.1005-3026.2025.20239058
    Abstract653)   HTML18)    PDF(pc) (1290KB)(170)       Save

    In fault diagnosis, the traditional time-frequency domain methods are easily affected by subjective factors while being used for feature extraction, so that the redundancy emerges. Deep learning algorithm is highly dependent on training data and has computation complexity. Fault diagnosis method for rolling bearings based on wavelet packet-thresholdless recurrence plot (WP-TRP)is proposed by combining time with frequency domains. Firstly, the decreasing information entropy criterion is developed to overcome the subjectivity of wavelet packet decomposition for acquisition of more accurate time-frequency feature. On this basis, the idea of thresholdless recurrence plot is introduced to extract the initial time domain feature. Moreover, by adopting the singular value decomposition to decrease the redundant feature, the computational efficiency can be increased. Secondly, the marine predator algorithm is introduced to obtain the optimal parameters of supporting vector machine, by which the more accurate classification can be realized. Finally, the effectiveness of the presented method is verified by using the simulation on the benchmark rolling bearing datasets.

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