Against the backdrop of globalization, the complexity of supply chains has been increasing, making supply chain resilience a critical factor in ensuring the stable operation of enterprises, national economies, and international trade. This paper adopts a network modeling perspective to systematically review the theoretical foundations and research progress in supply chain resilience, focusing on the application of network modeling methods. First, the concept of supply chain resilience is defined, and its developmental trajectory is reviewed. Through literature visualization analysis, this study delves into the current state of research on supply chain resilience, addressing challenges and risk management, highlighting the importance of network modeling techniques in this field. Subsequently, it explores supply chain network modeling based on complex networks and agent-based modeling, analyzing their strengths and limitations in simulating the overall evolution of supply chains and the dynamic behavior of individual entities. By integrating network structural characteristics with resilience evaluation methods, this paper suggests potential directions for future research. These include enhancing the description of individual firm behavior, analyzing the dynamics of information networks, and emphasizing task-oriented model design, thereby offering new perspectives and pathways for managing supply chain resilience in a way that can generate significant positive externalities for global economies. This research also indicates that the enhanced resilience of supply chains can produce a multiplier effect, benefiting not only individual firms but also promoting economic stability and growth across multiple countries.

This article proposes a fixed-time adaptive fault-tolerant control methodology for a larger class of high-order (powers are positive odd integers) nonlinear systems subject to asymmetric time-varying state constraints and actuator faults. In contrast with the state-of-the-art control methodologies, the distinguishing features of this study lie in that: (a) high-order asymmetric time-varying tan-type barrier Lyapunov function (BLF) is devised such that the state variables can be convergent to the preassigned compact sets all the time provided their initial values remain therein, which not only preserves the constraints satisfaction, but warrants the validity of the adopted neural network approximator; (b) the proposed control design ensures the tracking errors converge to specified residual sets within fixed time and makes the size of the convergence regions of tracking errors adjustable a priori by means of a new BLF-based tuning function and a projection operator; (c) a variable-separable lemma is delicately embedded into the control design to extract the control terms in a “linear-like” fashion which not only overcomes the difficulty that virtual control signals appear in a non-affine manner, but also solves the problem of actuator faults. Comparative simulations results finally validate the effectiveness of the proposed scheme.

The degree of ageing, the combustion kinetic performance and the change law of the combustion characteristics of ancient pine were experimentally investigated using thermogravimetric analysis, calorimetry experiments, component analysis experiments and gas chromatography-mass spectrometry analysis. The results show that the hemicellulose, cellulose, lignin and resin contents of the ancient pine decreased by 4.78%, 3.87%, 1.39% and 0.63%, respectively. The volatile content of the ancient pine decreased by 7.37%, while the ash content increased by 5.02%. The terpenes in the ancient pine were almost decomposed, and the calorific value of the ancient pine decreased by 21.54%. The ignition point of ancient pine was 19.83°C lower than that of the fresh pine. The activation energy and heat of the pre-charring stage of the combustion of the ancient pine were 1.37 times and 0.51 times those of the fresh pine, respectively. In contrast, the activation energy and heat of the charring stage of the combustion of the ancient pine were 0.66 times and 1.2 times those of the fresh pine, respectively. The ignition time and peak time of the ancient pine were shorter, and the heat release rate, smoke production rate, specific extinction area and CO production rate were higher than those of fresh pine.

In order to automate defect detection with few samples using unsupervised learning, this paper, considering materials commonly used in aircraft, proposes a phased array ultrasonic detection defect identification method using non-defect samples for training, and three-dimensional characterization is completed on this basis. A phased array ultrasonic device was used to detect two typical structures: a carbon fiber composite cylinder structure and a metal L-shaped structure. No damage label image was required, and the non-damaged sample was used as the the network training input. Based on contrast learning and the cross-registration loss of common features, a feature-matching network was constructed to extract the common features of undamaged detection data, and the performance was optimized by combining STN and GCNet modules. When the detection data of the sample were input to the aforementioned network, the defect distribution representing the location and rough shape of the defect was obtained through Mahalanobis distance calculation. The length was estimated using the S-scan image sequence sampling method. Additionally, the depth of the hole was estimated by combining the B-scan data with line recognition. According to the original model of the sample, the 3D characterization of defects was completed by pyautocad. In the experimental stage, three ablation experiments were carried out to verify the necessity of each module, and performance comparisons were mainly evaluated by F1 score and visualization using four existing well-known anomaly detection methods.

Based on the data analysis of the questionnaire, this paper finds the main problems of management teaching in military academy. We established Kano model to design the questionnaire, and analyzed the questionnaire results. The study found that there are some main problems in teaching, such as the lack of teaching pertinence, the lack of teaching depth, the lack of rich classroom organization forms, and the insignificant role of ideological education. We differentiate learners at different levels and integrate resources by using internal and external integration, command and technology integration and military civilian integration modes. Furthermore, this paper adopts the classroom innovation means of "interactive participation, game fun, immersion experience and curriculum ideology and politics", and puts forward the teaching mode of "two stages", "three aspects integration" and "four methods". The above methods can effectively improve the teaching effect of management course.

Modern war is sudden and rapid, and once the battle starts, the demand for equipment maintenance and guarantee will show an outburst situation, which puts equipment maintenance and guarantee capability to a severe test. To accurately grasp the civil-military integration of aviation maintenance and guarantee capabilities, it is necessary to evaluate their capabilities. To this end, the evaluation indicator system is established by using the three-dimensional indicator system construction model of "maintenance task → task demand → capability demand", and the evaluation method based on intuitionistic fuzzy entropy and normal grey cloud model is proposed, using intuitionistic fuzzy entropy theory to determine the expert weights and indicator weights, and the grey cloud model theory to determine the capability evaluation level. Finally, the application process of the evaluation model is demonstrated through specific examples, and the evaluation results are analyzed to verify the effectiveness of the evaluation model.