With the surge of global aviation traffic, the non-stop construction of restricted areas of large hub airports needs to balance facility upgrading and operation continuity. However, the coupling risk of construction and operation double system is prominent, and the existing evaluation has limitations such as insufficient theoretical adaptation and lack of dynamics. Based on the complex system theory and the principle of resilience engineering, this study constructs a three-dimensional evaluation model of "resisting-resilience-learning" and a system containing 27 indicators. The results show that the model can effectively describe the characteristics of resilience. The comprehensive resilience index of Pudong Airport is 0.800, and the optimal resilience and learning force are the main optimization directions. This study fills the gap of the specialized framework for safety resilience evaluation of non-stop construction in the airport exclusion zone, and provides an evaluation tool for similar projects.

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The Retrieval Enhanced Generation (RAG) system improves the accuracy and reliability of content generation by retrieving external knowledge, and has been widely used in fields such as intelligent question answering and knowledge assistants. However, its core performance depends on the quality of the retrieval stage, and the relevance and factual consistency of the retrieval results directly determine the effectiveness of the generated content. However, factors such as query complexity, document noise, and domain differences in real-world scenarios can easily lead to fluctuations in retrieval quality. Traditional manual evaluation is costly and outdated, making it difficult to meet real-time optimization requirements. At the same time, existing models have limitations in complex feature fusion and parameter optimization. Therefore, this article proposes a retrieval quality prediction model that combines the Lizard Optimization Algorithm (HLOA), Convolutional Neural Network (CNN), and Bidirectional Gated Recurrent Unit (BIGRU). Correlation analysis shows that there is a strong positive correlation between retrieval rank and retrieval usefulness score, meaning that the higher the retrieval rank, the better the retrieval usefulness score; The query complexity is strongly negatively correlated with the retrieval usefulness score, meaning that the higher the query complexity, the lower the retrieval usefulness score. Integrate this model with decision trees, random forests Adaboost, The comparison of nine models, including gradient boosting tree, ExtraTrees, CatBoost, XGBoost, LightGBM, and KNN, showed that their performance was overall better: MSE (28.617), RMSE (5.349), MAE (4.401), and MAPE (17.355) were the lowest, while R ² (0.952) was the highest. This study provides an effective solution for accurate prediction and real-time optimization of the retrieval quality of RAG systems, helping to enhance the application value of RAG technology in practical scenarios.

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Exploring a quantitative evaluation system for the popularity of digital media art has become a core issue that connects artistic creation, technological application and market communication. In view of the bottlenecks existing in the current algorithms, this paper proposes an LSTM algorithm optimized based on the multi-head attention mechanism. The study first conducted a correlation analysis. The results showed that the number of interactive elements had the strongest correlation with popularity, with an absolute value of the correlation coefficient reaching 0.556887. Variables such as creation time, the number of colors, and complexity scores also have a certain correlation with popularity. It can be seen that the time invested in creation, the richness of colors, and the complexity of the work will, to a certain extent, affect the popularity of the work. Taking decision tree, random forest, CatBoost, AdaBoost and XGBoost as the comparative experimental objects, in terms of various indicators, Our model performed the best in Accuracy, Recall, Precision, F1 and AUC. Its Accuracy is 0.855, which is higher than that of decision trees (0.709), random forests (0.803), CatBoost (0.786), AdaBoost (0.778), and XGBoost (0.744), with the highest overall classification accuracy. The Recall and Precision were 0.855 and 0.856 respectively, also leading other models. It performed better in identifying positive samples and the proportion of actually positive samples among those predicted to be positive. The F1 value of 0.855 is also higher than that of other models, and it has a stronger ability to balance accuracy and recall. The AUC reached 0.904, surpassing the 0.875 of random forest and the 0.876 of CatBoost, demonstrating the best ability to distinguish between positive and negative samples. In comparison, other models are slightly inferior in various indicators, especially the overall performance of the decision tree and XGBoost, which is more significantly lower than that of the Our model. This research achievement provides a more efficient method for the quantitative assessment of the popularity of digital media art. It not only offers direction for the integration of artistic creation and technological application but also provides a scientific basis for the formulation of market communication strategies.

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With social development, the process of electrification has accelerated accordingly. However, performance degradation of lithium-ion batteries caused by heat generation remains a major problem that needs to be overcome at present. Because lithium batteries perform best at room temperature on battery thermal-management systems, especially upgrades to interfacial thermal-conductive materials, concerns the battery’s efficiency, lifespan, and even safety. This paper reviews heat-generation mechanisms and sorts out three categories of nano-upgraded interfacial thermal-conductive materials: metal-based, phase-change, and fluid-based. The literature indicates that using metal nanowires with special alignment within polymers can enhance interfacial thermal-conductive performance by roughly 100 times. Adding about 1% weight fraction graphene to the matrix can improve the efficiency of the heat-transfer network, while the sensible heat is slightly reduced at the same time. Adding magnetic Fe₃O₄ or CuO to a fluid to construct modules of alternating-magnetic-field nanofluids, forming dynamic heat-conduction chains, can significantly reduce battery-module temperatures. This paper focuses on a comprehensive analysis of four aspects of the experimental materials: thermal-conductivity efficiency, heat-buffering capacity, practicality, and manufacturability. It provides material-level design guidelines for battery cooling systems that are safer, longer-lived, and supportive of faster charging.

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