Analytical Solution for 2D and 3D Lamb Problem in Saturated Soil Incorporating Effects of Compressibility of Solid and Pore Fluid

In this paper, to avoid the complexity, a simple and efficient analytical solution is derived for both 2D and 3D Lamb problems, respectively, in saturated soil under harmonic excitations. Unlike the existing solutions, the proposed solutions for both 2D and 3D Lamb problems in saturated soil under harmonic excitation are capable of well revealing the effect of compressibility of both liquid phase and solid phase on the ground displacements. By applying Fourier transforms and Hankel transforms on the governing equations of wave propagation in saturated soil, wave equations are transformed to ordinary differential equations. Combining the boundary conditions and draining conditions on the ground surface, the displacement solutions on the surface of saturated porous soil due to line and point harmonic excitations are derived, respec-tively. Then, the solutions in frequency domain are obtained by inverse integral transforms. In the meanwhile, for the sake of discussion without losing its generality, the non-dimensional solutions for three-dimensional Lamb problem are derived. The effectiveness and accuracy of the proposed solutions are demonstrated by employing three different approaches. Finally, parametric studies are conducted to investigate the effects of the governing parameters (i.e., exciting frequency, bulk modulus of soil matrix, and bulk modulus of pore fluid) on variation of non-dimensional displacement with the increasing distance away from the excitation source. The results indicate that, in contrast to the effect of the compressibility of soil matrix, the exciting frequency as well as the compressibility of the pore fluid play significant role in affecting the variation of displacement on ground surface subjected to excitations, which particularly highlights that the compressibility of the pore fluid should be carefully considered for evaluating the ground movements

3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses

3D-aware face generators are typically trained on 2D real-life face image datasets that primarily consist of near-frontal face data, and as such, they are unable to construct one-quarter headshot 3D portraits with complete head, neck, and shoulder geometry. Two reasons account for this issue: First, existing facial recognition methods struggle with extracting facial data captured from large camera angles or back views. Second, it is challenging to learn a distribution of 3D portraits covering the one-quarter headshot region from single-view data due to significant geometric deformation caused by diverse body poses. To this end, we first create the dataset 360{\deg}-Portrait-HQ (360{\deg}PHQ for short) which consists of high-quality single-view real portraits annotated with a variety of camera parameters (the yaw angles span the entire 360{\deg} range) and body poses. We then propose 3DPortraitGAN, the first 3D-aware one-quarter headshot portrait generator that learns a canonical 3D avatar distribution from the 360{\deg}PHQ dataset with body pose self-learning. Our model can generate view-consistent portrait images from all camera angles with a canonical one-quarter headshot 3D representation. Our experiments show that the proposed framework can accurately predict portrait body poses and generate view-consistent, realistic portrait images with complete geometry from all camera angles

Heterogeneous Catalysts from Natural Sources for Tar Removal: A Mini Review

Tar formation in gasifier is a headache problem in biomass gasification process. Catalytic cracking and/or reforming of tar using a catalyst is the most effective way to solve this problem. In order to reduce the cost, some heterogeneous catalysts from natural sources have been found to possess excellent catalytic properties that render them suitable for tar cracking and reforming in biomass gasification process. This article reviews the main natural catalysts such as dolomite, olivine, coal/biomass char and waste scallop shell that have been evaluated for tar removal in biomass gasification till date. Especially, our investigations on waste scallop shell based catalysts are reviewed in more details. The ways to improve the catalytic activity and appropriate options for a practical process are also reviewed and discussed. It is expected to provide the basis for a proposal for the exploitation of heterogeneous catalysts from natural sources to optimize tar removal in biomass gasification

Large uniform copper 1,3,5-benzenetricarboxylate metal-organic-framework particles from slurry crystallization and their outstanding CO 2 gas adsorption capacity

To prepare more and better metal organic frameworks (MOFs) from less solvent for capturing greenhouse gas, a modified slurry crystallization (MSC) method has been first demonstrated for making MOF copper 1, 3, 5-benzenetricarboxylate from a solvent-deficient system. One outstanding advantage is its drastic reduction of solvent consumption and waste liquid in the whole synthesis. In a typical process, the mass ratio of ethanol to the solid reactants is ∼0.52, which is only about 0.35%–7.5% of that used in conventional processes. A high yield of ∼98.0% is easily achieved for the product with uniform size up to 160 μm. The obtained MOFs demonstrate the characteristic microporous network with a surface area of ∼1851 m2 g−1 and a pore volume of ∼0.78 cm3 g−1, which benefit to adsorb high quantity of CO2 ∼ 6.73 mol kg−1 at ordinary pressure. X-ray diffraction studies indicate that the MOFs possess an outstanding diffraction intensity ratio of the crystal plane (2, 2, 2) to (2, 0, 0), I(222)/I(200) = 22.4. The MSC method provides a cost-effective approach for large-scale production of MOFs with more attractive properties than others. Most importantly, it can significantly cut down the waste liquid and production cost

Research on the Interaction between Tubeimoside 1 and HepG2 Cells Using the Microscopic Imaging and Fluorescent Spectra Method

The treatment of cancer draws interest from researchers worldwide. Of the different extracts from traditional Chinese medicines, Tubeimoside 1 (TBMS 1) is regarded as an effective treatment for cancer. To determine the mechanism of TBMS 1, the shape/pattern of HepG2 cells based on the microscopic imaging technology was determined to analyze experimental results; then the fluorescent spectra method was designed to investigate whether TBMS 1 affected HepG2 cells. A three-dimensional (3D) fluorescent spectra sweep was performed to determine the characteristic wave peak of HepG2 cells. A 2D fluorescent spectra method was then used to show the florescence change in HepG2 cells following treatment with TBMS 1. Finally, flow cytometry was employed to analyze the cell cycle of HepG2 cells. It was shown that TBMS 1 accelerated the death of HepG2 cells and had a strong dose- and time-dependent growth inhibitory effect on HepG2 cells, especially at the G2/M phase. These results indicate that the fluorescent spectra method is a promising substitute for flow cytometry as it is rapid and cost-effective in HepG2 cells

Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space

In recent years, the task of weakly supervised audio-visual violence detection has gained considerable attention. The goal of this task is to identify violent segments within multimodal data based on video-level labels. Despite advances in this field, traditional Euclidean neural networks, which have been used in prior research, encounter difficulties in capturing highly discriminative representations due to limitations of the feature space. To overcome this, we propose HyperVD, a novel framework that learns snippet embeddings in hyperbolic space to improve model discrimination. Our framework comprises a detour fusion module for multimodal fusion, effectively alleviating modality inconsistency between audio and visual signals. Additionally, we contribute two branches of fully hyperbolic graph convolutional networks that excavate feature similarities and temporal relationships among snippets in hyperbolic space. By learning snippet representations in this space, the framework effectively learns semantic discrepancies between violent and normal events. Extensive experiments on the XD-Violence benchmark demonstrate that our method outperforms state-of-the-art methods by a sizable margin.Comment: 8 pages, 5 figure