Numerical Analysis of Spreading Process of Ellipsoidal Spraying Droplet Impacting on Superhydrophobic Surface

Agricultural spray deposition is especially important for pesticide application because low efficiency can lead to environmental pollution, poor biological efficiency and economic loss. The deposition of pesticide spray on the leave surfaces is related to the impact kinetic behavior of droplets. But after considering the deformation of the droplet, how impingement will affect the deposition is an interesting research. In this study, a superhydrophobic surface was used to replace the plant surface that the pesticide droplets may affect. An interface tracking method was proposed to characterize the impingement dynamics behaviors of different ellipsoid droplets impacting on the surface. The maximum spreading coefficient and time of ellipsoidal droplets increased with the raise of their size. A lower sized droplet has a faster spreading rate, while the center of a higher sized droplet is thinner. As the velocity of pesticide increases, maximum spreading coefficient of droplet increases with a decrease in the maximum spreading time of droplet. The simulation results can contribute to provide theoretical basis for improving spray efficiency

Comparison of long-term radial artery occlusion following trans-radial coronary intervention using 6-french versus 7-french sheaths

Background: The aim of this study was to explore the impact of 6-Fr and 7-Fr sheaths on the incidenceof long-term radial artery occlusion (RAO) after trans-radial coronary intervention (TRI).Methods: From September 2013 to January 2016, patients with ischemic heart disease includingacute myocardial infarction and true bifurcation lesions were randomly assigned to 6-Fr group and7-Fr group immediately after coronary angiography in a 1:1 ratio. The radial artery diameters wereobserved by ultrasound examination one day prior to TRI as well as at 30 days and 1 year after TRI.The primary endpoint was the incidence of RAO at 1-year after TRI. The secondary endpoints were theincidence of local vascular complications during hospitalization and changes of radial artery diameterswithin 1-year after TRI between the two groups. Additionally, multivariate logistic regression analysiswas used to explore potential factors related to the incidence of long-term RAO after TRI.Results: A total of 214 patients were enrolled and randomly assigned to 6-Fr group (n = 105) or7-Fr group (n = 109). There was no significant difference in the incidence of RAO at 1-year after TRI(8.57% vs. 12.84%, p = 0.313). Moreover, no significant difference was observed in the incidence of localvascular complications during hospitalization (20% vs. 24.77%, p = 0.403). After 1-year follow-up,no significant difference was found in radial artery diameters (2.63 ± 0.31 mm vs. 2.64 ± 0.27 mm,p = 0.802). Multivariate logistic analysis revealed that repeated TRI was an independent risk factor oflong-term RAO 1 year after TRI (OR = 10.316, 95% CI 2.928–36.351, p = 0.001).Conclusions: Compared to 6-Fr sheath, 7-Fr sheath did not increase short-term or long-term incidenceof RAO after TRI

CLIP-based Synergistic Knowledge Transfer for Text-based Person Retrieval

Text-based Person Retrieval (TPR) aims to retrieve the target person images given a textual query. The primary challenge lies in bridging the substantial gap between vision and language modalities, especially when dealing with limited large-scale datasets. In this paper, we introduce a CLIP-based Synergistic Knowledge Transfer (CSKT) approach for TPR. Specifically, to explore the CLIP's knowledge on input side, we first propose a Bidirectional Prompts Transferring (BPT) module constructed by text-to-image and image-to-text bidirectional prompts and coupling projections. Secondly, Dual Adapters Transferring (DAT) is designed to transfer knowledge on output side of Multi-Head Attention (MHA) in vision and language. This synergistic two-way collaborative mechanism promotes the early-stage feature fusion and efficiently exploits the existing knowledge of CLIP. CSKT outperforms the state-of-the-art approaches across three benchmark datasets when the training parameters merely account for 7.4% of the entire model, demonstrating its remarkable efficiency, effectiveness and generalization.Comment: ICASSP2024(accepted). minor typos revision compared to version 1 in arxi

APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning framework that performs segmentation through matching each query feature to a learned class-specific prototype. However, this framework suffers from biased classification due to incomplete feature comparisons. To address this issue, we present an adaptive prototype representation by introducing class-specific and class-agnostic prototypes and thus construct complete sample pairs for learning semantic alignment with query features. The complementary features learning manner effectively enriches feature comparison and helps yield an unbiased segmentation model in the few-shot setting. It is implemented with a two-branch end-to-end network (\ie, a class-specific branch and a class-agnostic branch), which generates prototypes and then combines query features to perform comparisons. In addition, the proposed class-agnostic branch is simple yet effective. In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner. Extensive experiments on PASCAL-5 i and COCO-20 i demonstrate the superiority of our method. At no expense of inference efficiency, our model achieves state-of-the-art results in both 1-shot and 5-shot settings for few-shot semantic segmentation

APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning framework that performs segmentation through matching each query feature to a learned class-specific prototype. However, this framework suffers from biased classification due to incomplete feature comparisons. To address this issue, we present an adaptive prototype representation by introducing class-specific and class-agnostic prototypes and thus construct complete sample pairs for learning semantic alignment with query features. The complementary features learning manner effectively enriches feature comparison and helps yield an unbiased segmentation model in the few-shot setting. It is implemented with a two-branch end-to-end network (\ie, a class-specific branch and a class-agnostic branch), which generates prototypes and then combines query features to perform comparisons. In addition, the proposed class-agnostic branch is simple yet effective. In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner. Extensive experiments on PASCAL-5 i and COCO-20 i demonstrate the superiority of our method. At no expense of inference efficiency, our model achieves state-of-the-art results in both 1-shot and 5-shot settings for few-shot semantic segmentation

A Comparative Study on Damage Mechanism of Sandwich Structures with Different Core Materials under Lightning Strikes

Wind turbine blades are easily struck by lightning, a phenomenon that has attracted more and more attention in recent years. On this subject a large current experiment was conducted on three typical blade sandwich structures to simulate the natural lightning-induced arc effects. The resulting damage to different composite materials has been compared: polyvinyl chloride (PVC) and polyethylene terephthalate (PET) suffered pyrolysis and cracks inside, while the damage to balsa wood was fibers breaking off and large delamination between it and the resin layer, and only a little chemical pyrolysis. To analyze the damage mechanism on sandwich structures of different materials, a finite element method (FEM) model to calculate the temperature and pressure distribution was built, taking into consideration heat transfer and flow expansion due to impulse currents. According to the simulation results, PVC had the most severe temperature and pressure distribution, while PET and balsa wood were in the better condition after the experiments. The temperature distribution results explained clearly why balsa wood suffered much less chemical pyrolysis than PVC. Since balsa wood had better thermal stability than PET, the pyrolysis area of PET was obviously larger than that of balsa wood too. Increasing the volume fraction of solid components of porous materials can efficiently decrease the heat transfer velocity in porous materials. Permeability didn’t influence that much. The findings provide support for optimum material selection and design in blade manufacturing