To address the challenges of balancing accuracy and speed, as well as the parameters and FLOPs in current insulator defect detection, we propose an enhanced insulator defect detection algorithm, ML-YOLOv5, based on the YOLOv5 network. The backbone module incorporates depthwise separable convolution, and the feature fusion C3 module is replaced with the improved C2f_DG module. Furthermore, we enhance the feature pyramid network (MFPN) and employ knowledge distillation using YOLOv5m as the teacher model. Experimental results demonstrate that this approach achieved a 46.9% reduction in parameter count and a 43.0% reduction in FLOPs, while maintaining an FPS of 63.6. It exhibited good accuracy and detection speed on both the CPLID and IDID datasets, making it suitable for real-time inspection of high-altitude insulator defects.

Fog water collection, as a sustainable approach to alleviating water scarcity, has attracted considerable attention due to its low energy consumption and environmental friendliness. Various organisms in nature have evolved unique biological structures that efficiently capture and direct fog water. The fog water collection structures (FWCSs) and physical mechanisms of these organisms provide valuable inspiration for innovations in fog water collection technologies. This review systematically summarizes biomimetic structures designed for fog water collection, with a focus on representative natural examples such as the Namib desert beetle, cactus spines, spider silk, and Nepenthes mirabilis, highlighting how they achieve efficient fog water capture, coalescence, and transport through special surface textures, wettability regulation, and structural design. The underlying physical mechanisms are discussed in depth, including droplet behavior on micro/nanostructured surfaces, surface energy gradients, and Laplace pressure gradients in directional droplet transport. On this basis, the current challenges in bioinspired FWCSs design are outlined, and future perspectives are proposed. Future research may focus on the multiscale structural optimization of bioinspired FWCSs, the development of dynamically tunable designs, and the use of efficient and sustainable materials to further enhance fog water collection efficiency and ensure the long-term stability of FWCSs. Ultimately, by integrating modern manufacturing technologies and stimuli-responsive materials, bioinspired FWCSs hold great potential for applications in extreme environments, agricultural irrigation, and energy-efficient architecture, offering innovative solutions to the global water crisis.

A 3D manipulation technique based on two optothermally generated and actuated surface-bubble robots is proposed. A single laser beam can be divided into two parallel beams and used for the generation and motion control of twin bubbles. The movement and spacing control of the lasers and bubbles can be varied directly and rapidly. Both 2D and 3D operations of micromodules were carried out successfully using twin bubble robots. The cooperative manipulation of twin bubble robots is superior to that of a single robot in terms of stability, speed, and efficiency. The operational technique proposed in this study is expected to play an important role in tissue engineering, drug screening, and other fields.

This paper explores the propagation characteristics of perfect vortex beams (PVBs) in atmospheric turbulence and evaluates their performance as optical communication links. The aperture averaged scintillation index of PVBs under turbulent conditions is calculated, along with the mean signal-to-noise ratio (SNR) and mean bit error rate (BER) when employed as optical links. The influence of various PVB parameters on the aperture averaged scintillation index, mean SNR, and mean BER is systematically analyzed. The findings of this study are expected to aid in the advancement of optical systems operating in atmospheric turbulence conditions.