Publications by authors named "S C Tang"

Graphitic carbon nitride (g-C3N4) has gained significant attention as a promising nonmetallic semiconductor photocatalyst due to its photochemical stability, favorable electronic properties, and efficient light absorption. Nevertheless, its practical applications are hindered by limitations such as low specific surface area, rapid recombination of photogenerated charge carriers, poor electrical conductivity, and restricted photo-response ranges. This review explores recent advancements in the synthesis, modification and application of g-C3N4 and its nanocomposites with a focus on addressing these challenges.

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Background: Pulmonary arterial hypertension (PAH) is an incurable disease initiated by endothelial dysfunction, secondary to vascular inflammation and occlusive pulmonary arterial vascular remodeling, resulting in elevated pulmonary arterial pressure and right heart failure. Previous research has reported that dysfunction of type 2 bone morphogenetic protein receptor (BMPR2) signaling pathway in endothelium is inclined to prompt inflammation in PAH models, but the underlying mechanism of BMPR2 deficiency-mediated inflammation needs further investigation. This study was designed to investigate whether BMPR2 deficiency contributes to pulmonary arterial hypertension via the NLRP3 (NOD-like receptor family protein 3)/GSDME (gasdermin E)-mediated pyroptosis pathway.

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As a complex and dynamically regulated process, wound healing is collaboratively carried out by multiple types of cells. However, the precise mechanisms by which these cells contribute to immune regulation are not yet fully understood. Although research on bone regeneration has been quite extensive, the application of bioactive glass (BG) in skin tissue repair remains still relatively underexplored.

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Deep learning has achieved significant success in the field of defect detection; however, challenges remain in detecting small-sized, densely packed parts under complex working conditions, including occlusion and unstable lighting conditions. This paper introduces YOLOv8-n as the core network to propose VEE-YOLO, a robust and high-performance defect detection model. Firstly, GSConv was introduced to enhance feature extraction in depthwise separable convolution and establish the VOVGSCSP module, emphasizing feature reusability for more effective feature engineering.

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