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.

The life test of a complex electromechanical system (CEMS) is restricted by many factors, such as test time, test cost, test environment, test site, and test conditions. It is difficult to realize system reliability synthesis and prediction of a CEMS which consists of units with different life distributions. Aiming at the problems, a numerical analysis method based on the computer simulation and the Monte Carlo (MC) method is proposed. First, the unit’s life simulation values are simulated using the MC method with the given each unit’s life distribution and its distribution parameter point estimation. Next, using the unit’s life simulation values, the CEMS life simulation value can be obtained based on the CEMS reliability model. A simulation test is realized instead of the life test of the CEMS when there are enough simulation values of the CEMS life. Then, simulation data are analyzed, and the distribution of the CEMS life is deduced. The goodness-of-fit test, point estimation and confidence interval of the parameters, and reliability measure are estimated. Finally, as a test example of the wind turbine, the practicability and effectiveness of the method proposed in this paper are verified.

Drug-target interaction (DTI) prediction is essential for the development of novel drugs and the repurposing of existing ones. However, when the features of drug and target are applied to biological networks, there is a lack of capturing the relational features of drug-target interactions. And the corresponding multimodal models mainly depend on shallow fusion strategies, which results in suboptimal performance when trying to capture complex interaction relationships. Therefore, this study proposes a novel framework named KG-MACNF. This framework utilizes knowledge graph embedding (KGE) techniques to capture multi-level relational features of entities in large-scale biological networks. Simultaneously, our innovative PoolGAT network, along with CTD descriptors, is employed to extract drug structural features and protein sequence information. Finally, by employing our innovative nonlinear-driven cross-modal attention fusion network, the framework efficiently integrates these multimodal data and generates the final DTI prediction results. Experiments on two publicly available datasets, Yamanishi_08’s and BioKG, demonstrate the substantial advantages of KG-MACNF in DTI prediction. KG-MACNF demonstrates robust stability, especially under imbalanced data conditions. This study successfully overcomes the bottlenecks of prior models in utilizing modality information and feature complementarity, providing a more accurate tool for drug discovery and DTI prediction.

This study proposes a soft gripper with dielectric elastomer actuator (DEA) and 3D printing quadrilateral star honeycomb support due to the insufficient structural stiffness when grasping irregular objects. According to the bending requirement of the soft gripper and the electrostrictive properties of the dielectric elastomer (DE), a quadrangular star honeycomb structure with zero Poisson's ratio is proposed as the supporting structure for the soft gripper to enhance its stiffness. The mechanical properties of the honeycomb structure were analyzed via the finite element method (FEM) to improve its stress and horizontal compression displacement. By discussing different parameters such as the pre-stretching ratio of the dielectric elastomer and the pre-compression displacement of the honeycomb structure, we analyzed their influence on the bending displacement performance of the soft gripper. The performance of the soft grippers was verified through both simulations and experiments. The improvement soft gripper, composed of two-layer of dielectric elastomer with a 300% pre-stretching ratio and a honeycomb structure subjected to 20 mm pre-compression, exhibits a bending displacement of 13 mm on a voltage of 5000 V and can grasp objects weighting 5 g.