This paper presents an innovative model, VGGIncepNet, which integrates non-image-to-image conversion techniques with deep learning modules, specifically VGG16 and Inception, aiming to enhance performance in network intrusion detection and IoT security analysis. By converting non-image data into image data, the model leverages the powerful feature extraction capabilities of convolutional neural networks, thereby improving the multi-class classification of network attacks. We conducted extensive experiments on the NSL-KDD and CICIoT2023 datasets, and the results demonstrate that VGGIncepNet outperforms existing models, including BERT, DistilBERT, XLNet, and T5, across evaluation metrics such as accuracy, precision, recall, and F1-Score. VGGIncepNet exhibits outstanding classification performance, particularly excelling in precision and F1-Score. The experimental results validate VGGIncepNet’s adaptability and robustness in complex network environments, providing an effective solution for the real-time detection of malicious activities in network systems. This study offers new methods and tools for network security and IoT security analysis, with broad application prospects.

An ionic polymer–metal composite (IPMC) is a kind of soft material. The applications of IPMC in actuators, environmental sensing, and energy harvesting are currently increasing rapidly. In this study, an ordered Nafion nanofibre mat prepared by electrospinning was used to investigate the characteristics of the mechanoelectrical transduction of IPMC. The morphologies of the Nafion nanofibre mat were characterized. The proton conductivity, ion exchange capacities, and water uptake potential of the Nafion nanofibre mat were compared to traditional IPMC, respectively. A novel mechanism of Nafion nanofibre IPMC was designed and the open circuit voltage and short circuit current were measured. The maximum voltage value reached 100 mv. The output power was 3.63 nw and the power density was up to 42.4 μW/Kg under the load resistance. The Nafion nanofibre mat demonstrates excellent mechanoelectrcical transduction behavior compared to traditional IPMC and could be used for the development of self-powered devices in the future.

Based on ship navigational requirements and safety in foggy conditions and with a particular emphasis on avoiding ship collisions and improving navigational abilities, we constructed a fog navigation dataset along with a new method for enhancing foggy images and perceived visibility using a discriminant deep learning architecture and the EfficientNet neural network by replacing the SE module and incorporating a convolution block attention module and focal loss function. The accuracy of our model exceeded 95%, which meets the needs of an intelligent ship navigation environment in foggy conditions. As part of our research, we also determined the best enhancement algorithm for each type of fog according to its classification.

According to the advantages of ultrasonic vibration cutting, an ultrasound-assisted corneal trepanation robotic system is developed to improve the accuracy of corneal trephination depth and corneal incision quality in corneal trephination operations. Firstly, we analyzed the reasons for the difficulty in controlling the depth of trephination in corneal transplantations from the perspective of the biomechanical properties of the cornea. Based on the advantages of ultrasonic vibration cutting, we introduced an ultrasonic-vibration-assisted cutting method for corneal trephination and analyzed the cutting mechanism. Secondly, we described the surgical demands of corneal trephination and listed the design requirements of a robotic system. Thirdly, we introduced the design details of said system, including the system’s overall structure, the ultrasound-assisted end effector, the key mechanisms of the robotic system, and the human–machine interaction interface. We designed the end effector based on ultrasonic vibration cutting and its eccentric adjustment system in an innovative way. Additionally, we then presented a procedure for robot-assisted corneal trephination. Finally, we performed several cutting experiments on grapes and porcine eyeballs in vitro. The results show that, compared with manual trephine, ultrasound-assisted corneal trephination has a better operation effect on the accuracy of corneal trephination depth and corneal incision quality.

In order to solve the problem of uncontrollable cutting depth and the rough incision edge of the cornea with manual trephine in lamellar keratoplasty, an ultrasonic‐assisted corneal trephination method has been proposed for the first time in accordance with the advantage of ultrasonic vibration cutting, and the corresponding robotic system has been designed and researched. According to the traditional process of lamellar keratoplasty, the requirements of the surgical robotic system were first proposed. On this basis, the robotic system was designed and its schematic diagram was introduced. Second, the key components of the robotic body such as the eccentric adjusting mechanism and the end‐effector of ultrasonic scalpel were illustrated, which can realise corneal trephination of different incision diameters without scalpel replacement. Then the operation flow chart of a robot‐assisted lamellar keratoplasty was put forward. Finally, the preliminary verified experiments were performed using a grape and a porcine eyeball, respectively, in vitro with the prototype system. The results show that the robotic system can basically satisfy the operation requirements of lamellar keratoplasty. Owing to the less cutting force and smoother corneal incision edge of ultrasonic‐assisted lamellar keratoplasty compared with manual trephine, it was proved to be more feasible and superior.