As the most frequent wound complication, infection has become a major clinical challenge in wound management. To overcome the “Black Box” status of the wound‐healing process, next‐generation wound dressings with the abilities of real‐time monitoring, diagnosis during early stages, and on‐demand therapy has attracted considerable attention. Here, by combining the emerging development of bioelectronics, a smart flexible electronics‐integrated wound dressing with a double‐layer structure, the upper layer of which is polydimethylsiloxane‐encapsulated flexible electronics integrated with a temperature sensor and ultraviolet (UV) light‐emitting diodes, and the lower layer of which is a UV‐responsive antibacterial hydrogel, is designed. This dressing is expected to provide early infection diagnosis via real‐time wound‐temperature monitoring by the integrated sensor and on‐demand infection treatment by the release of antibiotics from the hydrogel by in situ UV irradiation. The integrated system possesses good flexibility, excellent compatibility, and high monitoring sensitivity and durability. Animal experiment results demonstrate that the integrated system is capable of monitoring wound status in real time, detecting bacterial infection and providing effective treatment on the basis of need. This proof‐of‐concept research holds great promise in developing new strategies to significantly improve wound management and other pathological diagnoses and treatments.

Virtual Force Algorithm (VFA) is becoming a main solution to area coverage forhomogeneous wireless sensor networks with random distribution of mobile sensor nodes.Consider the factors of the convergence, the boundary in Region Of Interest (ROI), effec-tive distance of acting force and useless moving, etc, VFA is improved to overcome the aboveproblems. Furthermore, an expression of exponential function for the relationship of vir-tual force is proposed to converge rapidly. Extensive simulation results indicate that theseimproved VFA get better performance in coverage rate, moving energy consumption, conver-gence etc. than original VFA.

With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective.

In this paper, we present a portable wireless electrocorticography (ECoG) system. It uses a high resolution 32-channel flexible ECoG electrodes array to collect electrical signals of brain activities and to stimulate the lesions. Electronic circuits are designed for signal acquisition, processing and transmission using Bluetooth Low Energy 4 (LTE4) for wireless communication with cell phone. In-vivo experiments on a rat show that the flexible ECoG system can accurately record electrical signals of brain activities and transmit them to cell phone with a maximal sampling rate of 30 ksampling/s per channel. It demonstrates that the epilepsy lesions can be detected, located and treated through the ECoG system. The wireless ECoG system has low energy consumption and high brain spatial resolution, thus has great prospects for future application.