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Accidents caused by vehicles changing lanes occur frequently on highways. Moreover, frequent lane changes can severely impact traffic flow during peak commuting hours and on busy roads. A novel framework based on a multi-relational graph convolutional network (MR-GCN) is herein proposed to address these challenges. First, a dynamic multilevel relational graph was designed to describe interactions between vehicles and road objects at different spatio-temporal granularities, with real-time updates to edge weights to enhance understanding of complex traffic scenarios. Second, an improved spatio-temporal interaction graph generation method was introduced, focusing on spatio-temporal variations and capturing complex interaction patterns to enhance prediction accuracy and adaptability. Finally, by integrating a dynamic multi-relational graph convolutional network (DMR-GCN) with dynamic scene sensing and interaction learning mechanisms, the framework enables real-time updates of complex vehicle relationships, thereby improving behavior prediction’s accuracy and real-time performance. Experimental validation on multiple benchmark datasets, including KITTI, Apollo, and Indian, showed that our algorithmic framework achieves significant performance improvements in vehicle behavior prediction tasks, with Map, Recall, and F1 scores reaching 90%, 88%, and 89%, respectively, outperforming existing algorithms. Additionally, the model achieved a Map of 91%, a Recall of 89%, and an F1 score of 90% under congested road conditions in a self-collected high-speed traffic scenario dataset, further demonstrating its robustness and adaptability in high-speed traffic conditions. These results show that the proposed model is highly practical and stable in real-world applications such as traffic control systems and self-driving vehicles, providing strong support for efficient vehicle behavior prediction.

In intelligent traffic monitoring systems, the significant distance between cameras and their non-overlapping fields of view leads to several issues. These include incomplete tracking results from individual cameras, difficulty in matching targets across multiple cameras, and the complexity of inferring the global trajectory of a target. In response to the challenges above, a deep learning-based vehicle tracking algorithm called FairMOT-MCVT is proposed. This algorithm con-siders the vehicles’ characteristics as rigid targets from a roadside perspective. Firstly, a Block-Efficient module is designed to enhance the network’s ability to capture and characterize image features across different layers by integrating a multi-branch structure and depth-separable convolutions. Secondly, the Multi-scale Dilated Attention (MSDA) module is introduced to improve the feature extraction capability and computational efficiency by combining multi-scale feature fusion and attention mechanisms. Finally, a joint loss function is crafted to better distinguish between vehicles with similar appearances by combining the trajectory smoothing loss and velocity consistency loss, thereby considering both position and velocity continuity during the optimization process. The proposed method was evaluated on the public UA-DETRAC dataset, which comprises 1210 video sequences and over 140,000 frames captured under various weather and lighting conditions. The experimental results demonstrate that the FairMOT-MCVT algorithm significantly enhances multi-target tracking accuracy (MOTA) to 79.0, IDF1 to 84.5, and FPS to 29.03, surpassing the performance of previous algorithms. Additionally, this algorithm expands the detection range and reduces the deployment cost of roadside equipment, effectively meeting the practical application requirements.

Many insect taxa cultivate fungi for food. Compared to well-known fungus cultivation in social insects, our knowledge on fungus cultivation in nonsocial insects is still limited. Here, we studied the nutritional potentials of the fungal cultivar, Penicillium herquei, for the larvae of its nonsocial insect farmer, Euops chinensis, a specialist on Japanese knotweed Reynoutria japonica. Overall, fungal hyphae and leaf rolls contained significantly higher carbon (C), stable isotopes of C (d13C), and nitrogen (d15N) but significantly lower C/N ratios compared to unrolled leaves, whereas insect bodies contained significantly higher N contents but lower C and C/N ratios compared to other types of samples. The MixSIAR model indicated that fungal hyphae contributed a larger proportion (0.626-0.797) to the diet of E. chinensis larvae than leaf materials. The levels of ergosterol, six essential amino acids, seven nonessential amino acids, and three B vitamins tested in fungal hyphae and/or leaf rolls were significantly higher than in unrolled leaves and/or larvae. The P. herquei genome contains the complete set of genes required for the biosynthesis of ergosterol, the essential amino acids valine and threonine, nine nonessential amino acids, and vitamins B2 and B3, whereas some genes associated with five essential and one nonessential amino acid were lost in the P. herquei genome. These suggest that P. herquei is capable of providing the E. chinensis larvae food with ergosterol, amino acids, and B vitamins. P. herquei appears to be able to synthesize or concentrate these nutrients considering that they were specifically concentrated in fungal hyphae. IMPORTANCE The cultivation of fungi for food has occurred across divergent insect lineages such as social ants, termites, and ambrosia beetles, as well as some seldomreported solitary insects. Although the fungal cultivars of these insects have been studied for decades, the dietary potential of fungal cultivars for their hosts (especially for those nonsocial insects) is largely unknown. Our research on the mutualistic system Euops chinensis-Penicillium herquei represents an example of the diverse nutritional potentials of the fungal cultivar P. herquei in the diet of the larvae of its solitary host, E. chinensis. These results demonstrate that P. herquei has the potential to synthesize or concentrate ergosterol, amino acids, and B vitamins and benefits the larvae of E. chinensis. Our findings would shed light on poorly understood fungal cultivation mutualisms in nonsocial insects and underscore the nutritional importance of fungal cultivars in fungal cultivation mutualisms. [ABSTRACT FROM AUTHOR]

Computer vision and deep learning are one of the main technologies for weed intelligent recognition in farmland. However, when using the deep learning technology to identify weeds in the field broomcorn millet growing at seedling stage, the weeds grow densely, which not only leads to the imbalance of positive and negative sample data, but also causes the problem of small area segmentation. In this paper, the combination of dice and focal loss was applied to Segformer semantic segmentation network with MiT-B3 as encoder block to identify intensive weeds in seedling broomcorn millet fields. The accuracy scores on the testing set were 95.23 %. The results show that the method proposed in this paper can effectively identify the seedling intensive weeds. [ABSTRACT FROM AUTHOR]