Forest fires pose a significant threat to forest resources and wildlife. To balance accuracy and parameter efficiency in forest fire detection, this study proposes an improved model, Mcan-YOLO, based on YOLOv7. In the Neck section, the asymptotic feature pyramid network (AFPN) was employed to effectively capture multi-scale information, replacing the traditional module. Additionally, the content-aware reassembly of features (CARAFE) replaced the conventional upsampling method, further reducing the number of parameters. The normalization-based attention module (NAM) was integrated after the ELAN-T module to enhance the recognition of various fire smoke features, and the Mish activation function was used to optimize model convergence. A real fire smoke dataset was constructed using the mean structural similarity (MSSIM) algorithm for model training and validation. The experimental results showed that, compared to YOLOv7-tiny, Mcan-YOLO improved precision by 4.6%, recall by 6.5%, and mAP50 by 4.7%, while reducing the number of parameters by 5%. Compared with other mainstream algorithms, Mcan-YOLO achieved better precision with fewer parameters.

A rapid reversed-phase high-performance liquid chromatographic (RP-HPLC) method was established for simultaneous determination of senecionine, senlciphylline, and senecionine N-oxide in a famous traditional Chinese medicine, Gynura segetum, which has been commonly used for hemostasis. The HPLC assay was performed on a Kromasil KR100-5 C₁₈ column (25 cm × 4.6 mm, 5 µm) with mobile phase composed of acetonitrile and 0.2% phosphoric acid–triethylamine within 40 min. The detection wavelength was 220 nm. All the compounds showed good linearity (r² > 0.9997). The method was reproducible with intra- and interday variation less than 2.82%. The recovery of the assay was in the range of 96.55–103.88%. The method was successfully applied to the quantification of three constituents in 15 Gynura segetum samples collected from different metropolis. The results indicated that the developed assay could be considered as a suitable quality-control method for Gynura segetum.

During autonomous driving, accurate detection of vehicles and pedestrians is important. Under foggy conditions, vehicle and pedestrian targets in visual sensor images often appear blurred, occluded, or with reduced contrast, which significantly hinders feature extraction. To address these challenges, in this paper, based on YOLO11, namely, DB‐YOLO, a vehicle and pedestrian detection algorithm for foggy scenes, is proposed. First, in accordance with the atmospheric scattering theory of imaging under foggy conditions, a dual‐backbone feature extraction structure is designed to implicitly model the degradation mechanism of foggy images, fuse high‐level and low‐level features, and gradually increase the receptive field, improving the representation ability of the model for foggy images. Second, a C3K2_iAFF module is introduced. This module employs an attention‐based data fusion approach to achieve more efficient feature fusion by suppressing irrelevant features that are polluted by haze while improving useful features that are related to the target. Additionally, an efficient upconvolution block (EUCB) upsampling method is used in the neck network to further suppress haze noise and improve key features. The experimental results reveal that DB‐YOLO achieved mAP50 values of 56.6% and 40% on two public foggy datasets, representing improvements of 8.1% and 2.5%, respectively, over the baseline model. The proposed approach has better detection performance than multiple state‐of‐the‐art (SOTA) models do. This study provides effective technical support for increasing the perceptual robustness of autonomous driving systems under adverse weather conditions.

Most of the existing video classification methods are based on large-scale data training, which can better realize the recognition and classification of known categories. However, data labelling is cumbersome and most things are unknown. Therefore, the existing video classification methods fall into a data bottleneck. This paper proposes a short video classification method based on GSM and semantic embedding. It uses super large-scale text information to assist the recognition process of the video classification model. This is an important development in the classification effect of knowledge categories. Specifically, this article expands the video classification method, adds category semantic embedding in the video feature extraction process, and trains to continuously fit the word vector of the corresponding category, and then uses semantic similarity to realize the classification of unknown categories. Multi-angle comparative experiments verify the effectiveness of this model, which can achieve good classification of unknown video categories.