Simulating Shear Wave Propagation in Two-Dimensional Fractured Heterogeneous Media by Coupling Boundary Element and Finite Difference Methods

A hybrid method to model the shear wave (SH) scattering from 2D fractures embedded in a heterogeneous medium is developed by coupling Boundary Element Method (BEM) and Finite Different Method (FDM) in the frequency domain. FDM is used to propagate an SH wave from a source through heterogeneities to localized homogeneous domains where fractures are embedded within artificial boundaries. According to Huygens’ Principle, the boundary points can be regarded as “secondary” point sources and their values are determined by FDM. Given the incident fields from these point sources, BEM is applied to model scatterings from fractures and propagate them back to the artificial boundaries. FDM then takes the boundaries as secondary sources and continues propagating the scattered field into the heterogeneous medium. The hybrid method utilizes both the advantage of BEM and FDM. A numerical iterative scheme is also presented to account for the multiple scattering between different sets of fractures. The results calculated from this hybrid method with pure BEM method are first compared to show the accuracy of the hybrid approach and the iterative scheme. This method is then applied to calculate the wave scattered from fractures embedded in complex media

Reality3DSketch: Rapid 3D Modeling of Objects from Single Freehand Sketches

The emerging trend of AR/VR places great demands on 3D content. However, most existing software requires expertise and is difficult for novice users to use. In this paper, we aim to create sketch-based modeling tools for user-friendly 3D modeling. We introduce Reality3DSketch with a novel application of an immersive 3D modeling experience, in which a user can capture the surrounding scene using a monocular RGB camera and can draw a single sketch of an object in the real-time reconstructed 3D scene. A 3D object is generated and placed in the desired location, enabled by our novel neural network with the input of a single sketch. Our neural network can predict the pose of a drawing and can turn a single sketch into a 3D model with view and structural awareness, which addresses the challenge of sparse sketch input and view ambiguity. We conducted extensive experiments synthetic and real-world datasets and achieved state-of-the-art (SOTA) results in both sketch view estimation and 3D modeling performance. According to our user study, our method of performing 3D modeling in a scene is $>$5x faster than conventional methods. Users are also more satisfied with the generated 3D model than the results of existing methods.Comment: IEEE Transactions on MultiMedi

SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, and More

The emergence of large models, also known as foundation models, has brought significant advancements to AI research. One such model is Segment Anything (SAM), which is designed for image segmentation tasks. However, as with other foundation models, our experimental findings suggest that SAM may fail or perform poorly in certain segmentation tasks, such as shadow detection and camouflaged object detection (concealed object detection). This study first paves the way for applying the large pre-trained image segmentation model SAM to these downstream tasks, even in situations where SAM performs poorly. Rather than fine-tuning the SAM network, we propose \textbf{SAM-Adapter}, which incorporates domain-specific information or visual prompts into the segmentation network by using simple yet effective adapters. Our extensive experiments show that SAM-Adapter can significantly elevate the performance of SAM in challenging tasks and we can even outperform task-specific network models and achieve state-of-the-art performance in the task we tested: camouflaged object detection and shadow detection. We believe our work opens up opportunities for utilizing SAM in downstream tasks, with potential applications in various fields, including medical image processing, agriculture, remote sensing, and more

Network pharmacology-based exploration identified the antiviral efficacy of Quercetin isolated from mulberry leaves against enterovirus 71 via the NF-κB signaling pathway

Introduction: Mulberry leaf (ML) is known for its antibacterial and anti-inflammatory properties, historically documented in “Shen Nong’s Materia Medica”. This study aimed to investigate the effects of ML on enterovirus 71 (EV71) using network pharmacology, molecular docking, and in vitro experiments.Methods: We successfully pinpointed shared targets between mulberry leaves (ML) and the EV71 virus by leveraging online databases. Our investigation delved into the interaction among these identified targets, leading to the identification of pivotal components within ML that possess potent anti-EV71 properties. The ability of these components to bind to the targets was verified by molecular docking. Moreover, bioinformatics predictions were used to identify the signaling pathways involved. Finally, the mechanism behind its anti-EV71 action was confirmed through in vitro experiments.Results: Our investigation uncovered 25 active components in ML that targeted 231 specific genes. Of these genes, 29 correlated with the targets of EV71. Quercetin, a major ingredient in ML, was associated with 25 of these genes. According to the molecular docking results, Quercetin has a high binding affinity to the targets of ML and EV71. According to the KEGG pathway analysis, the antiviral effect of Quercetin against EV71 was found to be closely related to the NF-κB signaling pathway. The results of immunofluorescence and Western blotting showed that Quercetin significantly reduced the expression levels of VP1, TNF-α, and IL-1β in EV71-infected human rhabdomyosarcoma cells. The phosphorylation level of NF-κB p65 was reduced, and the activation of NF-κB signaling pathway was suppressed by Quercetin. Furthermore, our results showed that Quercetin downregulated the expression of JNK, ERK, and p38 and their phosphorylation levels due to EV71 infection.Conclusion: With these findings in mind, we can conclude that inhibiting the NF-κB signaling pathway is a critical mechanism through which Quercetin exerts its anti-EV71 effectiveness

Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic standards

In this community effort, we compare measurements between 34 laboratories from 19 countries, utilizing mixtures of labelled authentic synthetic standards, to quantify by mass spectrometry four clinically used ceramide species in the NIST (National Institute of Standards and Technology) human blood plasma Standard Reference Material (SRM) 1950, as well as a set of candidate plasma reference materials (RM 8231). Participants either utilized a provided validated method and/or their method of choice. Mean concentration values, and intra- and inter-laboratory coefficients of variation (CV) were calculated using single-point and multi-point calibrations, respectively. These results are the most precise (intra-laboratory CVs ≤ 4.2%) and concordant (inter-laboratory CVs < 14%) community-derived absolute concentration values reported to date for four clinically used ceramides in the commonly analyzed SRM 1950. We demonstrate that calibration using authentic labelled standards dramatically reduces data variability. Furthermore, we show how the use of shared RM can correct systematic quantitative biases and help in harmonizing lipidomics. Collectively, the results from the present study provide a significant knowledge base for translation of lipidomic technologies to future clinical applications that might require the determination of reference intervals (RIs) in various human populations or might need to estimate reference change values (RCV), when analytical variability is a key factor for recall during multiple testing of individuals

Corrosion Behavior of High Entropy Alloys and Their Application in the Nuclear Industry&mdash;An Overview

With multiple principal components, high entropy alloys (HEAs) have aroused great interest due to their unique microstructures and outstanding properties. Recently, the corrosion behavior of HEAs has become a scientific hotspot in the area of material science and engineering, and HEAs can exhibit good protection against corrosive environments. A comprehensive understanding of the corrosion mechanism of HEAs is important for further design of HEAs with better performance. This paper reviews the corrosion properties and mechanisms of HEAs (mainly Cantor alloy and its variants) in various environments. More crucially, this paper is focused on the influences of composition and microstructure on the evolution of the corrosion process, especially passive film stability and localized corrosion resistance. The corrosion behavior of HEAs as structural materials in nuclear industry applications is emphasized. Finally, based on this review, the possible perspectives for scientific research and engineering applications of HEAs are proposed

A New Time–Frequency Feature Extraction Method for Action Detection on Artificial Knee by Fractional Fourier Transform

With the aim of designing an action detection method on artificial knee, a new time&#8722;frequency feature extraction method was proposed. The inertial data were extracted periodically using the microelectromechanical systems (MEMS) inertial measurement unit (IMU) on the prosthesis, and the features were extracted from the inertial data after fractional Fourier transform (FRFT). Then, a feature vector composed of eight features was constructed. The transformation results of these features after FRFT with different orders were analyzed, and the dimensions of the feature vector were reduced. The classification effects of different features and different orders are analyzed, according to which order and feature of each sub-classifier were designed. Finally, according to the experiment with the prototype, the method proposed above can reduce the requirements of hardware calculation and has a better classification effect. The accuracies of each sub-classifier are 95.05%, 95.38%, 91.43%, and 89.39%, respectively; the precisions are 78.43%, 98.36%, 98.36%, and 93.41%, respectively; and the recalls are 100%, 93.26%, 86.96%, and 86.68%, respectively