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Hypergraphs have become an accurate and natural expression of high-order coupling relationships in complex systems. However, applying high-order information from networks to vital node identification tasks still poses significant challenges. This paper proposes a von Neumann entropy-based hypergraph vital node identification method (HVC) that integrates high-order information as well as its optimized version (semi-SAVC). HVC is based on the high-order line graph structure of hypergraphs and measures changes in network complexity using von Neumann entropy. It integrates s -line graph information to quantify node importance in the hypergraph by mapping hyperedges to nodes. In contrast, semi-SAVC uses a quadratic approximation of von Neumann entropy to measure network complexity and considers only half of the maximum order of the hypergraph's s -line graph to balance accuracy and efficiency. Compared to the baseline methods of hyperdegree centrality, closeness centrality, vector centrality, and sub-hypergraph centrality, the new methods demonstrated superior identification of vital nodes that promote the maximum influence and maintain network connectivity in empirical hypergraph data, considering the influence and robustness factors. The correlation and monotonicity of the identification results were quantitatively analyzed and comprehensive experimental results demonstrate the superiority of the new methods. At the same time, a key non-trivial phenomenon was discovered: influence does not increase linearly as the s -line graph orders increase. We call this the saturation effect of high-order line graph information in hypergraph node identification. When the order reaches its saturation value, the addition of high-order information often acts as noise and affects propagation. [ABSTRACT FROM AUTHOR]

The pressurized capsule structure provides the pressure environment for astronauts or payloads in space, which is thus considered as the most crucial structural component for manned spacecraft. The manned deep space exploration mission (MDSEM) brings new challenges to the pressurized capsule structure: extremely low structural weight, long service life, reusability and adaptability to the harsh deep space environment. The conventional welded panel pressurized capsule structure (WPPCS) is not able to meet these new requirements. To address the above challenges, this paper comprehensively expounds why the current WPPCS cannot meet the requirements of MDSEMs based on the analysis of the vibration environment and structural characteristics of the pressurized capsule structure. Furthermore, a new type of integrated panel pressurized capsule structure (IPPCS) is proposed, showing the lightweight advantage compared with WPPCS. Finally, the technical details and research results of the strength criterion, design method, material upgrading and structural integrity manufacturing process of the IPPCS are fully introduced. The conclusions drawn in this paper will provide useful and meaningful references for the future development of large-size, lightweight pressurized capsule structures. [ABSTRACT FROM AUTHOR]

In the composite structure of spacecraft, the honeycomb sandwich structure is the basic bearing component used to bear and transmit loads. To explore the influencing factors on the bearing capacity of honeycomb sandwich structures, this study combines local tests and speckle measurement systems to conduct tensile tests on 10 test specimens with different parameters. Firstly, a comprehensive assessment was conducted on the accuracy of the loading and measurement system, the rationality of the testing method, and the mechanical properties of the test piece. It was found that the maximum measurement error of the speckle measurement system did not exceed 0.01 mm, and the differences between the yield load and failure load measured using different inner diameters of the compression ring were 0.15% and 3.84%, respectively. This indicates that the measurement system is accurate and that the influence of the inner diameter of the compression ring can be ignored. Moreover, it was found that considering the accuracy retention ability of the structure under load, the allowable load of the embedded parts is about 90% of the yield load. Finally, the data of specimens with different parameters were compared and it was found that the strength of the honeycomb sandwich structure is directly proportional to the thickness of the skin, the density of the honeycomb core cells, and the size of the embedded parts. [ABSTRACT FROM AUTHOR]

Glass fiber trapezoidal corrugated sandwich structures are composed of trapezoidal cores and glass fiber-reinforced polymer (GFRP) panels. A series of low-velocity impact tests were conducted to investigate the impact resistance performance, considering the effects of impact position, impactor shape, and impactor diameter on the damage mechanism of sandwich structures. When the impactor shape and impact energy remain constant, the maximum impact load at the node impact point is higher than at the base, while the displacement of impact at the base is significantly higher than that at the node. Secondly, when the impactor diameter and energy of the impactor are the same, the hemispherical impactor requires less penetration energy to impact the sandwich structure compared to the flat impactor. Comparing the shape of the impactor, it is found that the smaller the contact surface of the impactor, the more concentrated the stress, and the lower the required penetration energy. Finally, when the impactor shape and impact energy are the same, as the diameter of impactor increases, the damage expansion during impacting on the sandwich structure becomes more sufficient, resulting in decreased impact displacement and smaller impact damage caused to the sandwich structure. [ABSTRACT FROM AUTHOR]

In recent decades, the explosion of the waste generation rate and corresponding environmental impacts worldwide have turned waste management into one of the most vital services in urban areas to alleviate the waste-related issues. In this study, a novel integrated model is developed to improve the municipal solid waste system by considering the facility location, shift scheduling, and vehicle routing decisions. The problem is formulated as a tri-objective mixed-integer linear programming model, striving to optimize the sustainable development goals in the waste system. These objectives encompass the total profit, air pollution emissions, citizen satisfaction, and social risk factors. The findings from this study illustrate that the proposed integrated framework empowers decision makers to maintain the resilience of the municipal solid waste system by concurrently addressing three critical sustainability aspects. [ABSTRACT FROM AUTHOR]

As one of the most complex and critical components of spacecraft, the structural design of the curved cabin door faces two challenges. On the one hand, it is difficult to obtain innovative configurations for the cabin door in the preliminary design stage. On the other hand, the traditional optimization design algorithm is inefficient in the detailed design stage. In this paper, a two-stage intelligent method for the layout design of the curved cabin door is proposed. In the first stage, the innovative stiffener layout of the cabin door is obtained based on the topology optimization method. Then the mesh deformation method is used for rapid modeling and geometric reconstruction. In the second stage, a recently proposed powerful evolutionary algorithm, named elite-driven surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (ES-CMA-ES), is employed to optimize the parameters of the cabin door and its surrounding thin-wall structure. To verify the effectiveness of the proposed method, a curved cabin door example from the spacecraft (cargo spaceship) is carried out. Compared with the traditional orthogrid stiffener design, the mass of the optimal design is reduced by 52.21% while satisfying the constraints, which indicates the excellent optimization ability of the proposed method and demonstrates huge potential for improving the carrying capacity and efficiency of the spacecraft. [ABSTRACT FROM AUTHOR]

As the key load-bearing component of spacecraft, the strength evaluation of stiffened plate structures faces two challenges. On the one hand, the simulation results are sometimes inaccurate, due to the simplification of the true loading conditions and modeling details. On the other hand, data from the sensors cannot provide the full-field strength information of the structure, which may result in the misjudgment of the structural state. To this end, a digital twin modeling method of multi-source data fusion based on transfer learning is proposed in this paper. In transfer learning, simulation data and sensor data are utilized as the source dataset and the target dataset, respectively. First, a pre-trained deep neural network (DNN) model is established based on the source dataset. Then, the pre-trained DNN model is fine-tuned based on the target dataset using a lower learning rate and fewer training epochs. Finally, a digital twin model can be built, which is capable of visualizing the full-field strength information of the stiffened plate structure. To verify the effectiveness of the proposed method, an experimental study on a hierarchical stiffened plate is carried out. Compared with the traditional data fusion method, the results verify the high prediction accuracy and efficiency of the proposed method, demonstrating its potential for the strength health monitoring of spacecraft in orbit. [ABSTRACT FROM AUTHOR]