Solid-phase organic synthesis of polyprenols with a traceless sulfone linker is described. The polymer-bound benezenesulfinate is first linked with the “tail” building blocks of isoprenyl chlorides via S-alkylation. With use of dimsyl anion as an appropriate base, the polymer-bound α-sulfonyl carbanion is generated and coupled with other “body” building blocks in an efficient manner. After repeated processes and a global palladium-catalyzed desulfonation with LiEt₃BH as the reducing agent, the desired polyprenols with various chain lengths and geometrical configurations are obtained in 32−59% overall yields. The solid-phase synthesis offers the advantage in facile isolation of polyprenols without tedious operation or time-consuming purification.

The high‐level semantic information extracted from the pedestrian attribute feature is an important element for pedestrian recognition. Pedestrian attribute recognition plays an important role in both intelligent video surveillance and pedestrian re‐identification promoting the convenience of searching and performance of model. This paper tries finding a practical method to improve the performance of the pedestrian re‐identification by combining pedestrian attributes and identities. The multi‐task learning method combines pedestrian recognition and attribute information in a direct way that considers the correlation between pedestrian attributes and identities but ignores the principle and degree of such correlation. To solve this problem, a new pedestrian recognition framework based on attribute mining and reasoning is proposed in this paper. To enhance the expression ability of attribute features, it designs spatial channel attention module (SCAM) based on attention mechanism to extract features from every attribute. SCAM can not only locate the attributes on the feature map, but also effectively mine channel features with a higher degree of association with attributes. In addition, both spatial attention model and channel attention model are integrated by multiple groups of parallel branches, which further improve the network performance. Finally, using the semantic reasoning and information transmission function of graph convolutional network, the relationship between attribute features and pedestrian features can be mined. Besides, pedestrian features with stronger expression ability can also be obtained. Experiment work is conducted in two databases, DukeMTMC‐reID and Market‐1501, which are commonly used in pedestrian recognition tasks. On the Market‐1501 dataset, the final effect of the algorithm model CMC‐1 can reach 94.74%, and mAP can reach 87.02%; on the DukeMTMC‐reID dataset, CMC‐1 can reach 87.03%, and mAP can reach 77.11%. The results show that our method is at the top of the existing pedestrian recognition methods.

With the rapid development of mobile internet technology, the explosive growth of image–text multimodal data generated by social networking platforms has provided rich practical scenarios and theoretical research value for multimodal sentiment analysis. However, existing methods generally suffer from inefficient modal interaction and imperfect sentiment aggregation mechanisms, particularly an over-reliance on visual modalities, leading to an imbalance in cross-modal semantic correlation modeling. To address these issues, this paper proposes a sentiment analysis algorithm for image–text modalities based on curriculum learning and attention mechanisms. The algorithm introduces the concept of curriculum learning, fully considering the negative impact of irrelevant images in image–text data on overall sentiment analysis, effectively suppressing interference from irrelevant visual information without requiring manual data cleaning. Meanwhile, the algorithm designs a dual-stage attention architecture—first capturing cross-modal correlation features via cross-modal attention, then introducing an attention bottleneck strategy to compress redundant information flow, achieving efficient feature fusion by constraining intra-modal attention dimensions. Finally, extensive experiments were conducted on two public datasets, demonstrating that the proposed method outperforms existing approaches in sentiment prediction performance.

Due to the challenges posed by limited size and features, positional and noise issues, and dataset imbalance and simplicity, small object detection is one of the most challenging tasks in the field of object detection. Consequently, an increasing number of researchers are focusing on this area. In this paper, we propose a Directional Enhanced Adaptive (DEA) detection framework for small targets. This framework effectively combines the detection accuracy advantages of two-stage methods with the detection speed advantages of one-stage methods. Additionally, we introduce a Multi-Scale Object Adaptive Slicing (MASA) module and an improved IoU-based aggregation module that integrate with this framework to enhance detection performance. For better comparison, we use the F1 score as one of the evaluation metrics. The experimental results demonstrate that our DEA framework improves the performance of various backbone detection networks and achieves better comprehensive detection performance than other proposed methods, even though our network has not been trained on the test dataset while others have.