Experimental investigation on the surface and subsurface damages characteristics and formation mechanisms in ultra-precision grinding of SiC

Surface and subsurface damages appear inevitably in the grinding process, which will influence the performance and lifetime of the machined components. In this paper, ultra-precision grinding experiments were performed on Reaction-bonded Silicon Carbide (RB-SiC) ceramics to investigate surface and subsurface damages characteristics and formation mechanisms in atomic scale. The surface and subsurface damages were measured by a combination of scanning electron microscopy (SEM), atomic force microscopy (AFM), raman spectroscopy and transmission electron microscope (TEM) techniques. Ductile-regime removal mode is achieved below critical cutting depth, exhibiting with obvious plough stripes and pile-up. The brittle fracture behavior is noticeably influenced by the microstructures of RB-SiC such as impurities, phase boundary and grain boundary. It was found that subsurface damages in plastic zone mainly consist of stacking faults (SFs), twins and limited dislocations. No amorphous structure can be observed in both 6H-SiC and Si particles in RB-SiC ceramics. Additionally, with the aid of high resolution TEM analysis, SFs and twins were found within the 6H-SiC closed packed plane i.e. (0001). At last, based on the SiC structure characteristic, the formation mechanisms of SFs and twins was discussed, and a schematic model was proposed to clarify the relationship between plastic deformation induced defects and brittle fractures

Bidirectional relationship between Helicobacter pylori infection and nonalcoholic fatty liver disease: insights from a comprehensive meta-analysis

BackgroundHelicobacter pylori (H. pylori) infection and nonalcoholic fatty liver disease (NAFLD) represent significant concerns in global health. However, the precise relationship between H. pylori and NAFLD remains a subject of ongoing debate. This study endeavors to elucidate the association between H. pylori infection and the susceptibility to NAFLD. Furthermore, we aim to investigate the interplay among H. pylori infection, NAFLD, and metabolic syndrome (MetS).MethodsWe conducted an extensive search of the PubMed, EMBASE, and Web of Science databases spanning from inception to January 2024. Our examination focused on rigorous studies investigating the correlation between H. pylori infection and NAFLD. Utilizing a random-effects model, we computed the pooled odds ratio (OR) and corresponding 95% confidence interval (CI). Additionally, we assessed statistical heterogeneity, performed sensitivity analyses, and scrutinized the potential for publication bias.ResultsThirty-four studies involving 175,575 individuals were included in our meta-analysis. Among these, 14 studies (involving 94,950 patients) demonstrated a higher incidence of NAFLD in H. pylori infection-positive individuals compared to H. pylori infection-negative individuals [RR = 1.17, 95% CI (1.10, 1.24), Z = 4.897, P < 0.001]. Seventeen studies (involving 74,928 patients) indicated a higher positive rate of H. pylori infection in patients with NAFLD compared to those without NAFLD [RR = 1.13, 95% CI (1.02, 1.24), Z = 2.395, P = 0.017]. Sensitivity analyses confirmed the robustness of these findings, and funnel plot analysis revealed no significant publication bias. Furthermore, we observed associations between H. pylori infection or NAFLD and various metabolic factors, including body mass index (BMI), blood pressure, lipids, liver function, and kidney function.ConclusionOur meta-analysis presents evidence supporting a reciprocal relationship between H. pylori infection and the susceptibility to NAFLD. Nevertheless, additional investigations are warranted to bolster this correlation and unravel the underlying mechanisms involved

S-NeRF++: Autonomous Driving Simulation via Neural Reconstruction and Generation

Autonomous driving simulation system plays a crucial role in enhancing self-driving data and simulating complex and rare traffic scenarios, ensuring navigation safety. However, traditional simulation systems, which often heavily rely on manual modeling and 2D image editing, struggled with scaling to extensive scenes and generating realistic simulation data. In this study, we present S-NeRF++, an innovative autonomous driving simulation system based on neural reconstruction. Trained on widely-used self-driving datasets such as nuScenes and Waymo, S-NeRF++ can generate a large number of realistic street scenes and foreground objects with high rendering quality as well as offering considerable flexibility in manipulation and simulation. Specifically, S-NeRF++ is an enhanced neural radiance field for synthesizing large-scale scenes and moving vehicles, with improved scene parameterization and camera pose learning. The system effectively utilizes noisy and sparse LiDAR data to refine training and address depth outliers, ensuring high quality reconstruction and novel-view rendering. It also provides a diverse foreground asset bank through reconstructing and generating different foreground vehicles to support comprehensive scenario creation. Moreover, we have developed an advanced foreground-background fusion pipeline that skillfully integrates illumination and shadow effects, further enhancing the realism of our simulations. With the high-quality simulated data provided by our S-NeRF++, we found the perception methods enjoy performance boost on several autonomous driving downstream tasks, which further demonstrate the effectiveness of our proposed simulator