A high-resolution study of particle export in the southern South China Sea based on Th-234 : U-238 disequilibrium

During a spring intermonsoon cruise in 2004, depth profiles of total and particulate Th-234 in the upper 100 m were collected at 36 stations in the southern South China Sea (SCS), covering a surface area of similar to 1.0 x 10(6) km(2). Thorium-234 was sampled by using a modified small-volume MnO2 co-precipitation technique, which allows mapping the Th-234 distribution with a high spatial resolution. A stratified structure of Th-234/U-238 disequilibria was generally observed in the upper 100 m water column, suggesting that the euphotic zone of the southern SCS in this season can be separated into two layers: an upper layer with low export production rates and a lower layer with high export production rates. At the same time, we observed extensive zones of Th-234 excess within the euphotic layer, which is possibly due to intense remineralization of particulate matter. Particulate organic carbon (POC) export was estimated from a three-dimensional steady state model of Th-234 fluxes combined with measurements of the POC/Th-234 ratio on suspended particles. The POC export for this region varied from a low of -10.7 +/- 1.5 mmolC m(-2) d(-1) to a high of 12.6 +/- 1.1 mmolC m(-2) d(-1), with an average of 3.8 +/- 4.0 mmolC m(-2) d(-1). A negative flux of POC export is interpreted as the result of lateral input of particulate matter from nearby waters. Regional patterns in POC export show enhanced fluxes along the western and southern boundaries of the study region, and a "tongue'' of low export extending northwestward from similar to 7 degrees N 116 degrees E to similar to 10 degrees N 111 degrees E. This geographic distribution is consistent with the overall surface circulation pattern of the southern SCS in this season

Purification, Preliminary Characterization and Hepatoprotective Effects of Polysaccharides from Dandelion Root

In this study, purification, preliminary characterization and hepatoprotective effects of water-soluble polysaccharides from dandelion root (DRP) were investigated. Two polysaccharides, DRP1 and DRP2, were isolated from DRP. The two polysaccharides were α-type polysaccharides and didn’t contain protein. DRP1, with a molecular weight of 5695 Da, was composed of glucose, galactose and arabinose, whereas DRP2, with molecular weight of 8882 Da, was composed of rhamnose, galacturonic acid, glucose, galactose and arabinose. The backbone of DRP1 was mainly composed of (1→6)-linked-α-d-Glc and (1→3,4)-linked-α-d-Glc. DRP2 was mainly composed of (1→)-linked-α-d-Ara and (1→)-linked-α-d-Glc. A proof-of-concept study was performed to assess the therapeutic potential of DRP1 and DRP2 in a mouse model that mimics acetaminophen (APAP) -induced liver injury (AILI) in humans. The present study shows DRP1 and DRP2 could protect the liver from APAP-induced hepatic injury by activating the Nrf2-Keap1 pathway. These conclusions demonstrate that the DRP1 and DRP2 might be suitable as functional foods and natural drugs in preventing APAP-induced liver injury

Dynamic Gesture Recognition Based on Three-Stream Coordinate Attention Network and Knowledge Distillation

Gesture recognition has always been one of the important research directions in the field of computer vision. The dynamic gesture has the problems of complex backgrounds and many interference factors. The gesture recognition model based on deep learning usually has high computational cost and poor real-time performance. In addition, deep learning models are limited to recognizing existing categories in the training set and their performance largely depends on the amount of labeled data. To address the above problems, this paper presents a dynamic gesture recognition method named 3SCKI based on a three-stream coordinate attention (CA) network, knowledge distillation, and image-text contrastive learning. Specifically, 1) CA is utilized for feature fusion to make the model focus more on target gestures and reduce background interference, 2) traditional knowledge distillation loss is improved to reduce the amount of calculation and improve the real-time performance. Specifically, the guidance function is added to make the student network only learn the classification probability correctly identified by the teacher network, and 3) multi-granularity context prompt template integration method is proposed to construct an improved CLIP visual language model MG-CLIP. It aligns text and visual concepts from the image level to the object level to the part level. Through comparative learning of image features and text features, gesture classification is performed, enabling the model to identify image categories that have not appeared during the training phase. The proposed method is evaluated on the ChaLearn LAP large-scale isolated gesture dataset (IsoGD). The results show that our proposed method can obtain recognition rates of 65.87% on the validation set of IsoGD. For single mode data, 3SCKI can obtain the state-of-the-art recognition accuracy on RGB, Depth, and Optical Flow data (61.22%, 58.84%, and 50.30% of the validation set of IsoGD, respectively)

Hybrid-Supervised-Learning-Based Automatic Image Segmentation for Water Leakage in Subway Tunnels

Quickly and accurately identifying water leakage is one of the important components of the health monitoring of subway tunnels. A mobile vision measurement system consisting of several high-resolution, industrial, charge-coupled device (CCD) cameras is placed on trains to implement structural health monitoring in tunnels. Through the image processing technology proposed in this paper, water leakage areas in subway tunnels can be found and repaired in real time. A lightweight automatic segmentation approach to water leakage using hybrid-supervised-deep-learning technology is proposed. This approach consists of the weakly supervised learning Water Leakage-CAM and fully supervised learning WRDeepLabV3+. The Water Leakage-CAM is used for the automatic labeling of data. The WRDeepLabV3+ is used for the accurate identification of water leakage areas in subway tunnels. Compared with other end-to-end semantic segmentation networks, the hybrid-supervised learning approach can more completely segment the water leakage region when dealing with water leakage in complex environments. The hybrid-supervised-deep-learning approach proposed in this paper achieves the highest MIoU of 82.8% on the experimental dataset, which is 6.4% higher than the second. The efficiency is also 25% higher than the second and significantly outperforms other end-to-end deep learning approaches

Hybrid-Supervised-Learning-Based Automatic Image Segmentation for Water Leakage in Subway Tunnels

Quickly and accurately identifying water leakage is one of the important components of the health monitoring of subway tunnels. A mobile vision measurement system consisting of several high-resolution, industrial, charge-coupled device (CCD) cameras is placed on trains to implement structural health monitoring in tunnels. Through the image processing technology proposed in this paper, water leakage areas in subway tunnels can be found and repaired in real time. A lightweight automatic segmentation approach to water leakage using hybrid-supervised-deep-learning technology is proposed. This approach consists of the weakly supervised learning Water Leakage-CAM and fully supervised learning WRDeepLabV3+. The Water Leakage-CAM is used for the automatic labeling of data. The WRDeepLabV3+ is used for the accurate identification of water leakage areas in subway tunnels. Compared with other end-to-end semantic segmentation networks, the hybrid-supervised learning approach can more completely segment the water leakage region when dealing with water leakage in complex environments. The hybrid-supervised-deep-learning approach proposed in this paper achieves the highest MIoU of 82.8% on the experimental dataset, which is 6.4% higher than the second. The efficiency is also 25% higher than the second and significantly outperforms other end-to-end deep learning approaches

An Image Recommendation Algorithm Based on Target Alternating Attention and User Affiliation Network

Currently, how to exploit the deep features of images in image recommender systems to achieve image enhancement still needs further research. In addition, little research has explored the implicit and increasing preferences of users by using the affiliation generated by indirect users and virtual users of the main users, which leads to the phenomenon of information cocoon. An Image Recommendation Algorithm Based on Target Alternating Attention and User Affiliation Network (TAUA) is proposed in this paper that addresses the problems of inadequate extraction of semantic features in an image and information cocoon in image recommender systems. First, to complete the multi-dimensional description of the image, we extract the category, color, and style features of the image through a multi-channel convolutional neural network (MCNN), and we then perform migration and integration on these features. Then, to enhance the pixel-level representation ability of the image and achieve image feature enhancement, we propose target alternating attention to capture the information of surrounding pixels alternately from inside to outside. Finally, a user affiliation network, including indirect users and virtual users, is established according to the user behavior and transaction record, and the users’ increasing preferences and affiliated users are mined through the implicit interaction relationship of users. Experimental results show that compared with baselines on the Amazon dataset, the results of F@10, NDCG@10, and AUC of the proposed algorithm are 4.02%, 5.00%, and 2.14% higher than those of ACF, and 5.76%, 0.86% and 1.16% higher than those of VPOI. On the Flickr dataset, our algorithm outperforms ACF by 5.74%, 5.12%, and 3.68% in F@10, NDCG@10, and AUC, respectively, and outperforms VPOI by 0.45%, 0.47%, and 0.49%. TAUA has better recommendation performance and can significantly improve the recommendation effect