Nowadays, drug–target interactions (DTIs) prediction is a fundamental part of drug repositioning. However, on the one hand, drug–target interactions prediction models usually consider drugs or targets information, which ignore prior knowledge between drugs and targets. On the other hand, models incorporating priori knowledge cannot make interactions prediction for under-studied drugs and targets. Hence, this article proposes a novel dual-network integrated logistic matrix factorization DTIs prediction scheme (Ro-DNILMF) via a knowledge graph embedding approach. This model adds prior knowledge as input data into the prediction model and inherits the advantages of the DNILMF model, which can predict under-studied drug–target interactions. Firstly, a knowledge graph embedding model based on relational rotation (RotatE) is trained to construct the interaction adjacency matrix and integrate prior knowledge. Secondly, a dual-network integrated logistic matrix factorization prediction model (DNILMF) is used to predict new drugs and targets. Finally, several experiments conducted on the public datasets are used to demonstrate that the proposed method outperforms the single base-line model and some mainstream methods on efficiency.

The soft gripper has received extensive attention, due to its good adaptability and flexibility. The dielectric elastomer (DE) actuator as a flexible electroactive polymer that provides a new approach for soft grippers. However, they have the disadvantage of having a poor rigidity. Therefore, the optimization design method of a rigid-flexible soft finger is presented to improve the rigidity of the soft finger. We analyzed the interaction of the rigid and soft materials, using the finite element method (FEM), and researched the influence of the parameters (compression of the spring and pre-stretching ratio of the DE) on the bending angle. The optimal parameters were obtained using the FEM. We experimentally verified the accuracy of the proposed method. The maximum bending angle is 19.66°. Compared with the theoretical result, the maximum error is 3.84%. Simultaneously, the soft gripper with three fingers can grasp various objects and the maximum grasping quality is 11.21 g.