Due to the lack of timely protection measures against infectious diseases, or based on the particularity of the transmission of some infectious diseases and the time-varying connections between people, the transmission dynamics of infectious diseases in the information society are becoming more and more complex and changeable. A fractional-order epidemic mathematical model with network weighting and latency is proposed in this paper, and the stability near the disease-free equilibrium point and endemic equilibrium point are discussed separately. Subsequently, an event-triggered impulsive control strategy based on an infection rate threshold is put forward. By selecting the appropriate control gain, the Zeno phenomenon can be eliminated on the premise of ensuring the stability of the control error system. Finally, the theoretical results were validated numerically and some conclusions are presented. These findings contribute to future research on the limited-time event-triggered impulsive control of infectious diseases.

Oriented object detection (OOD) can precisely detect objects with arbitrary direction in remote sensing images (RSIs). Up to now, the two-stage OOD methods have attracted more attention because of their high detection accuracy. However, the two-stage methods only rely on the features of each proposal for object recognition, which leads to the misclassification problem because of the intra-class diversity, inter-class similarity and clutter backgrounds in RSIs. To address the above problem, an OOD model combining scene classification is proposed. Considering the fact that each foreground object has a strong contextual relationship with the scene of the RSI, a scene classification branch is added to the baseline OOD model, and the scene classification result of input RSI is used to exclude the impossible categories. To focus on the hard instances and enhance the consistency between classification and regression, a task-aligned focal loss (TFL) which combines the classification difficulty with the regression loss is proposed, and TFL assigns lager weights to the hard instances and optimizes the classification and regression branches simultaneously. The ablation study proves the effectiveness of scene classification branch, TFL and their combination. The comparisons with 15 and 14 OOD methods on the DOTA and DIOR-R datasets validate the superiority of our method.

The You Only Look Once (YOLO) object detection model has been widely applied to electric power operation violation identification, owing to its balanced performance in detection accuracy and inference speed. However, it still faces the following challenges: (1) insufficient detection capability for irregularly shaped objects; (2) objects with low object-background contrast are easily omitted; (3) improving detection accuracy while maintaining computational efficiency is difficult. To address the above challenges, a novel real-time object detection model is proposed in this paper, which introduces three key innovations. To handle the first challenge, an edge perception cross-stage partial fusion with two convolutions (EPC2f) module that combines edge convolutions with depthwise separable convolutions is proposed, which can enhance the feature representation of irregularly shaped objects with only a slight increase in parameters. To handle the second challenge, an adaptive combination of local and global features module is proposed to enhance the discriminative ability of features while maintaining computational efficiency, where the local and global features are extracted respectively via 1D convolutions and adaptively combined by using learnable weights. To handle the third challenge, a parameter sharing of a multi-scale detection heads scheme is proposed to reduce the number of parameters and improve the interaction between multi-scale detection heads. The ablation study on the Ali Tianchi competition dataset validates the effectiveness of three innovation points and their combination. EAP-YOLO achieves the mAP@0.5 of 93.4% and an mAP@0.5–0.95 of 70.3% on the Ali Tianchi Competition dataset, outperforming 12 other object detection models while satisfying the real-time requirement.