Flexible tactile sensors show promise for artificial intelligence applications due to their biological adaptability and rapid signal perception. Triboelectric sensors enable active dynamic tactile sensing, while integrating static pressure sensing and real-time multichannel signal transmission is key for further development. Here, we propose an integrated structure combining a capacitive sensor for static spatiotemporal mapping and a triboelectric sensor for dynamic tactile recognition. A liquid metal-based flexible dual-mode triboelectric-capacitive-coupled tactile sensor (TCTS) array of 4 × 4 pixels achieves a spatial resolution of 7 mm, exhibiting a pressure detection limit of 0.8 Pa and a fast response of 6 ms. Furthermore, neuromorphic computing using the MXene-based synaptic transistor achieves 100% recognition accuracy of handwritten numbers/letters within 90 epochs based on dynamic triboelectric signals collected by the TCTS array, and cross-spatial information communication from the perceived multichannel tactile data is realized in the mixed reality space. The results illuminate considerable application possibilities of dual-mode tactile sensing technology in human-machine interfaces and advanced robotics.

The present work aims to develop a digital twin system typical of intelligent manufacturing applications, which has integrated visualization technologies, as well as the process parameter simulation solution. The application under consideration is a typical machining process, with a gantry machine tool controlled by Siemens Programmable Logic Controller(PLC) S7-1200. With the establishment of dual-directional data communication between the physical machine tool and its virtual counterpart based on TCP/IP protocol, real-time visualization, monitoring, and control of the entire working process can be achieved. Furthermore, we integrated with the digital twin system as a solution for real-time process parameter simulation based on finite element modeling (FEM), which enables the real-time monitoring of necessary process parameters, e.g., surface deformation, during the machining process. A preliminary experiment was conducted to validate our proposed digital twin system, and the results demonstrated that our proposed method has satisfactory performance in terms of both control and monitoring of the traditional machining process, and synchronization between the physical and virtual models is also proven to be positive with minimal latency.

The locations of acupuncture points (acupoints) differ among human individuals due to variations in factors such as height, weight and fat proportions. However, acupoint annotation is expert-dependent, labour-intensive, and highly expensive, which limits the data size and detection accuracy. In this paper, we introduce the “AcuSim” dataset as a new synthetic dataset for the task of localising points on the human cervicocranial area from an input image using an automatic render and labelling pipeline during acupuncture treatment. It includes a creation of 63,936 RGB-D images and 504 synthetic anatomical models with 174 volumetric acupoints annotated, to capture the variability and diversity of human anatomies. The study validates a convolutional neural network (CNN) on the proposed dataset with an accuracy of 99.73% and shows that 92.86% of predictions in validation set align within a 5mm threshold of margin error when compared to expert-annotated data. This dataset addresses the limitations of prior datasets and can be applied to applications of acupoint detection and visualization, further advancing automation in Traditional Chinese Medicine (TCM).

In response to the challenges in the assembly process of prefabricated buildings and the pursuit of sustainable construction, this paper proposes an industrial robot arm teleoperation method based on mixed reality (MR) technology. Traditional assembly processes that rely on manual installation face challenges such as operator safety issues, and high energy consumption. Our proposed method utilizes MR technology to achieve immersive and remote control and uses Optimal Resolved Acceleration (ORA) and Resolved Acceleration Control (RAC) to complement the shortcomings of Resolved-Rate Motion Controller (RRMC) by introducing compliance control to enhance environmental adaptability and telemanipulation accuracy. The aluminum profile assembly and precision testing experiments have shown the feasibility and accuracy of this method. The MR teleoperation framework allows for compliance control, and despite some noise, the robotic arm exhibits good accuracy. Future work will focus on expanding task complexity, improving system accuracy, integrating intelligent functions, and exploring scalability for large-scale construction projects.