In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and effectively preventing risks before they occur. The use of flight monitoring data for flight anomaly detection is effective in discovering unknown and potential flight incidents. In this paper, we propose a time-feature attention mechanism and construct a deep hybrid model for flight anomaly detection. The hybrid model combines a time-feature attention-based convolutional autoencoder with the HDBSCAN clustering algorithm, where the autoencoder is constructed and trained to extract flight features while the HDBSCAN works as an anomaly detector. Quick access record (QAR) flight data containing information of aircraft landing at Kunming Changshui International and Chengdu Shuangliu International airports are used as the experimental data, and the results show that (1) the time-feature-based convolutional autoencoder proposed in this paper can better extract the flight features and further discover the different landing patterns; (2) in the representation space of the flights, anomalous flight objects are better separated from normal objects to provide a quality database for subsequent anomaly detection; and (3) the discovered flight patterns are consistent with those at the airports, resulting in anomalies that could be interpreted with the corresponding pattern. Moreover, several examples of anomalous flights at each airport are presented to analyze the characteristics of anomalies.

Existing research on crude oil trade networks has largely focused on their static structural characteristics. However, systematic quantitative assessments of network stability, as well as examinations of the relationship between structural characteristics and stability, remain relatively limited. Based on United Nations Commodity Trade Statistics from 2000 to 2023, this study constructs an international crude oil trade network. By integrating complex network analysis with spatiotemporal methods, the study examines the network’s evolutionary patterns, evaluates its stability from both topological and flow dimensions using Jaccard similarity and weighted similarity indicators, and applies Spearman’s rank correlation to explore the associations between structural characteristics and stability. The results indicate that the following: (1) the international crude oil trade network exhibits a hierarchical core-periphery structure, with overall trade linkages between countries becoming increasingly tight; (2) key inflection points in network stability are correlated with major international events, and these correlations manifest differently across the dimensions of trade structure and trade flows; and (3) modularity is negatively correlated with topological stability and positively correlated with flow stability, while degree assortativity shows no significant association with either topological stability or flow stability, revealing that although the core-periphery structure enhances the resilience of trade scale, it may simultaneously weaken the flexibility of partnerships.