More and more manufacturing companies are talking about what’s often called the circular economy—in which businesses can create supply chains that recover or recycle the resources used to ...

Motivated by these challenges, we propose an innovative prediction algorithm named dual-channel graph and Hypergraph Convolutional Network (DCGHCN) to discover microbes underlying disease traits.

We propose a traffic speed prediction model based on dynamic structural prior (DSP) ST graph attention networks. We provide a structural prior graph, namely, dual graph convolution, which combines ...

The Multi-level Graph Convolution Neural Network (MLGCN) model is an ultra-efficient approach for 3D point cloud analysis that utilizes shallow Graph Neural Networks (GNN) blocks to extract features ...

Therefore, it has the potential to provide neurosurgeons with rapid and reliable decision support, especially in emergency conditions. The knowledge graph enhanced deep-learning model can exhibit ...

As an ideal impact energy absorber, the external inversion process of thin-walled metal tubes over circular dies has been studied theoretically since 1960s. However, in the most existing theoretical ...

In this paper, we design a new model, Conv2DGCN, that combines GCN and 2D convolution. Conv2DGCN obtains rich feature interactions through 2D convolution, which allows nodes to aggregate more ...