Skin tissue suffering from severe damages fail in self-regeneration. Proper wound dressings are highly demanded to protect the wound region and accelerate the healing process. Although large efforts have been devoted, there still exist disturbing dilemmas for traditional dressings. The exquisite design of bio-interface upon superwettable materials opens new avenues and addresses the problems perfectly. However, the advancements in this area have rarely been combed. In light of this, this minireview attempts to summarize recent strategies of superwettable bio-interfaces for wound care. Concentrating on the management of biofluids (blood and exudate), we described superwettable hemostatic bio-interfaces first, and then introduced the management of exudates. Finally, the perspective of this area was given. This minireview gives a comprehensive outline for readers and is believed to provide references for the design of superwettable materials in biomedical area.

The application of UAVs is becoming increasingly extensive. However, high-precision autonomous landing is still a major industry difficulty. The current algorithm is not well-adapted to light changes, scale transformations, complex backgrounds, etc. To address the above difficulties, a deep learning method was here introduced into target detection and an attention mechanism was incorporated into YOLOX; thus, a UAV positioning algorithm called attention-based YOLOX (A-YOLOX) is proposed. Firstly, a novel visual positioning pattern was designed to facilitate the algorithm’s use for detection and localization; then, a UAV visual positioning database (UAV-VPD) was built through actual data collection and data augmentation and the A-YOLOX model detector developed; finally, corresponding high- and low-altitude visual positioning algorithms were designed for high- and low-altitude positioning logics. The experimental results in the actual environment showed that the AP50 of the proposed algorithm could reach 95.5%, the detection speed was 53.7 frames per second, and the actual landing error was within 5 cm, which meets the practical application requirements for automatic UAV landing.

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