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To overcome the limitations of inadequate local feature representation and the underutilization of global information in dynamic graph convolutions, we propose a network that combines attention mechanisms with dual graph convolutions. Firstly, we construct a static graph based on the dynamic graph using the K-nearest neighbors algorithm and geometric distances of point clouds. This integration of dynamic and static graphs forms a dual graph structure, compensating for the underutilization of geometric positional relationships in the dynamic graph. Next, edge convolutions are applied to extract edge features from the dual graph structure. To further enhance the capturing ability of local features, we employ attention pooling, which combines max pooling and average pooling operations. Secondly, we introduce channel attention modules and spatial self-attention modules to improve the representation ability of global features and enhance semantic segmentation accuracy in our network. Experimental results on the S3DIS dataset demonstrate that compared to dynamic graph convolution alone, our proposed approach effectively utilizes both semantic and geometric relationships between point clouds using dual graph convolutions while addressing limitations related to insufficient local feature extraction. The introduction of attention mechanisms helps mitigate underutilization issues with global information, resulting in significant improvements in model performance. [ABSTRACT FROM AUTHOR]

The characteristics of carbides, such as type, size, and distribution, are important factors that affect the quality of high‐speed steel. To improve the characteristics of carbides produced by the electroslag remelting (ESR) process, an innovative process of electroslag remelting withdrawal with secondary aerosol cooling (ESRW–SAC) is proposed. The structure of the molten bath is obtained using tungsten powder detection, and the characteristics of carbides are statistically quantified by scanning electron microscopy (SEM), X‐ray diffraction (XRD), and the Image‐Pro Plus quantitative analysis software. The 3D carbide morphologies and carbides types are determined by an electrolysis device and SEM–energy‐dispersive spectroscopy (EDS). The results show that, compared with the traditional process, without SAC, the structure of the molten metal bath becomes shallower and flatter with the ESRW–SAC process. Under the influence of SAC, the network spacing and lamellar thickness of carbides considerably decrease, the phase area of carbides increases, and the distribution of carbides becomes more uniform. After the ESRW–SAC process, M2C and M6C carbides are still dominant; however, the morphology of M2C carbides changes from a lamellar to a curved short‐rod form. [ABSTRACT FROM AUTHOR]

Inspired by energy conversion and waste reuse, hybridized Ni-MOF derivative-CdS-DETA/g-C 3 N 5 , a type-II heterojunction photocatalyst, is synthesized by a hydrothermal method for simultaneous and highly efficient photocatalytic degradation and hydrogen evolution in dye wastewater. Without the addition of cocatalysts and sacrificial agents, the optimal MOF-CD(2)/CN5 (i.e. Ni-MOF derivative-CdS-DETA (20 wt.%)/g-C 3 N 5 ) exhibit good bifunctional catalytic activity, with a H 2 evolution rate of 2974.4 µmol g -1  h -1 during the degradation of rhodamine B (RhB), and a removal rate of 99.97% for RhB. In the process of H 2 -evolution-only, triethanolamine is used as a sacrificial agent, exhibiting a high H 2 evolution rate (19663.1 µmol g -1  h -1 ) in the absence of a cocatalyst, and outperforming most similar related materials (such as MOF/g-C 3 N 5 , MOF-CdS, CdS/g-C 3 N 5 ). With the help of type-II heterojunction, holes are scavenged for the oxidative degradation of RhB, and electrons are used in the decomposition of water for H 2 evolution during illumination. This work opens a new path for photocatalysts with dual functions of simultaneous efficient degradation and hydrogen evolution., (© 2024 Wiley‐VCH GmbH.)