Search

Point cloud registration is one of the fundamental tasks in computer vision, but faces challenges under low overlap conditions. Recent approaches use transformers and overlapping masks to improve perception, but mask learning only considers Euclidean distances between features, ignores mismatches caused by fuzzy geometric structures, and is often computationally inefficient. To address these issues, we introduce a novel matching framework. Firstly, we fuse adaptive graph convolution with PPF features to obtain rich feature perception. Subsequently, we construct a PGT framework that uses GeoTransformer and combines it with location information encoding to enhance the geometry perception between source and target clouds. In addition, we improve the visibility of overlapping regions through information exchange and the AIS module, aiming at subsequent keypoint extraction, preserving points with distinct geometrical structures while suppressing the influence of non-overlapping regions to improve computational efficiency. Finally, the mask is refined through contrast learning to preserve geometric and distance similarity, which helps to compute the transformation parameters more accurately. We have conducted comprehensive experiments on synthetic and real-world scene datasets, demonstrating superior registration performance compared to recent deep learning methods. Our approach shows remarkable improvements of 68.21% in R R M S E and 76.31% in t R M S E on synthetic data, while also excelling in real-world scenarios with enhancements of 76.46% in R R M S E and 45.16% in t R M S E . [ABSTRACT FROM AUTHOR]

Implants and other medical devices are prone to bacterial infections on their surface due to bacterial attachment and biofilm formation. In this study, silver nanoparticles were generated in situ onto regulated synthesized polydopamine particles, and the optimal amount of silver nitrate was determined. Composite micro–nano particles were then deposited on a titanium alloy surface. X-ray photoelectron spectroscopy and energy-dispersive X-ray spectroscopy were used to confirm that the titanium alloy surface was successfully coated with PDA-Ag. Scanning electron microscopy, transmission electron microscopy, and three-dimensional optical profilometry were utilized to analysis the morphology of the micro–nano particles and the surface morphology after deposition. The diameters of the polydopamine particles and silver nanoparticles were 150 nm and 25 nm, respectively. The surface roughness values decreased from 0.357 μm to 25.253 μm because of the coated PDA-Ag. Morphology and chemical composition analyses of the modified surface indicated that the PDA-Ag particles were uniformly bonded to the substrate surface. Antimicrobial assays illustrated that the PDA-Ag-modified surface possessed resistance against Escherichia coli and Staphylococcus aureus attachment, with an effectiveness of 96.14 and 85.78%, respectively. This work provides a new strategy and theoretical basis for tackling medical-related surface infections caused by bacterial adhesion. [ABSTRACT FROM AUTHOR]

Open caisson foundations are often used in large-span bridge construction because of their advantageous, and an open caisson foundation gradually sinks to a predetermined position via earth excavation. Excessive sinking speed may result in various construction risks (e.g. inclination and structural damage of the open caisson). To prevent these risks, it is important to analyse the sinking situation of the foundation and predict the sinking speed during the sinking process. A sinking speed prediction model is proposed based on the gradient boosting decision tree (GBDT) algorithm, and the model can extract the data features of the structural stress monitoring data to predict the sinking speed of open caissons. Taking the supersized open caisson foundation in the Changtai Yangtze River Bridge Project as a case study, the proposed model was validated by using the monitoring data of this foundation. The validation results of this project indicate that the proposed model has high prediction accuracy, short time consumption, a good prediction effect and high practicability. Based on the model's prediction, an earth excavation scheme can be flexibly adjusted to prevent the potential construction risks of open caissons caused by excessive sinking speed. [ABSTRACT FROM AUTHOR]

Silver nanoparticles (AgNPs) are known for their antibacterial properties and their ability to promote wound healing. By incorporating silver nanoparticles into medical gauze, the resulting composite material shows promise as an advanced wound dressing. However, clinical applications are hindered by challenges related to the stability of silver nanoparticle loading on the gauze as nanoparticle leaching can compromise antibacterial efficacy. In this study, silver nanoparticles were immobilized onto polydopamine (PDA) submicron particles, which were then used to modify medical gauze. Energy dispersive spectroscopy (EDS) was employed to analyze the elemental distribution on the modified gauze, confirming successful surface modification. The antibacterial properties of the modified gauze were assessed using a laser scanning confocal microscope (CLSM). The results demonstrated a significant reduction in the adhesion rates of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) by 99.1% and 63%, respectively, on the PDA–Ag-modified gauze. Optical density (OD) measurements at 590 nm indicated that the modified gauze effectively inhibited biofilm formation, underscoring its potent antimicrobial capabilities. Further antibacterial efficacy was evaluated by diluting and plating co-cultured bacterial solutions with the modified dressing, followed by 24 h incubation and colony counting. The gauze exhibited an antibacterial efficiency of 99.99% against E. coli and 99.8% against S. aureus. Additionally, cell compatibility tests, involving the co-culture of PDA–Ag composites with human cells, demonstrated excellent biocompatibility. These findings suggest that PDA–Ag-modified medical gauze holds significant potential for the treatment of infected wounds, offering a promising solution to improve wound care through enhanced antimicrobial activity and biocompatibility. [ABSTRACT FROM AUTHOR]

Stress is a crucial factor that affects the permeability, and the influence of stress on permeability has been studied extensively by many investigators. In this article, the relationship between confining pressure and permeability is investigated. For this purpose, a void ratio–pressure model based on the theory of incompressible elastic porous media is first proposed, then, based on the model and Kozeny–Carman equation, a new pressure–permeability relationship is proposed. Finally the relationship is utilized to predict the permeability of coal and mudstone under different confining pressures, and these predictions are compared with other models and found to be in better agreement with experimental data reported recently. [ABSTRACT FROM AUTHOR]