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Object detection in aerial drone imagery has attracted increasing attention in Unmanned Aerial Vehicle(UAV) sensing applications. However, small objects occupying limited image regions, with large scale variations and similar background interference, make it challenging to perceive them. Meanwhile, the constrained computing power of the onboard platform imposes requirements on the speed and efficiency of the algorithm. In this paper, we propose an efficient object detection network for real-time UAV perception named ESO-Det. Our approach introduces three key innovations: (1) Dense Cross-branch Complementary Module, a lightweight model that dynamically integrates semantic and spatial information to improve the network’s understanding of scene details. (2) Large-Kernel Context Integration Module, a module that expands receptive fields to effectively aggregate multi-scale contextual information. (3) Lightweight Selective Aggregation Module, a model selectively aggregates fused multi-scale features through different functional branches. Extensive experiments demonstrate that the proposed method achieves higher performance than representative existing approaches while maintaining real-time processing capability. The results show that our method is suitable for real-time UAV object detection.

This research aimed to study the sound absorption properties of natural fibers and their reinforced composites. Sound absorption coefficients of three types of natural fibers, i.e., ramie, flax and jute fibers and their composites were measured by the two-microphone transfer function technique in the impedance tube. The results were compared with synthetic fibers and their composites. It was found that both natural fibers and their composites had superior capability of noise reduction. The multi-scale and hollow lumen structures of natural fibers contributed to the high sound absorption performance. Moreover, the sound absorption properties of these natural fibers were also calculated by the Delany-Bazley and Garai-Pompoli models. They showed good agreement with the experimental data. It was concluded that multi-functional composite materials can be made by natural fibers so that both the mechanical and acoustical functions can be achieved.

Various super-resolution imaging techniques have been developed to break the diffraction-limited resolution of light microscopy. However, it still remains challenging to obtain three-dimensional (3D) super-resolution information of structures and dynamic processes in live cells at high speed. We recently developed high-speed single-point edge-excitation sub-diffraction (SPEED) microscopy and its two-dimensional (2D)-to-3D transformation algorithm to provide an effective approach to achieving 3D sub-diffraction-limit information in subcellular structures and organelles that have rotational symmetry. In contrast to most other 3D super-resolution microscopy or 3D particle-tracking microscopy approaches, SPEED microscopy does not depend on complex optical components and can be implemented onto a standard inverted epifluorescence microscope. SPEED microscopy is specifically designed to obtain 2D spatial locations of individual immobile or moving fluorescent molecules inside sub-micrometer biological channels or cavities at high spatiotemporal resolution. After data collection, post-localization 2D-to-3D transformation is applied to obtain 3D super-resolution structural and dynamic information. The complete protocol, including cell culture and sample preparation (6–7 d), SPEED imaging (4–5 h), data analysis and validation through simulation (5–13 h), takes ~9 d to complete. This protocol describes how to implement high-speed single-point edge-excitation sub-diffraction (SPEED) microscopy in combination with 2D-to-3D transformation to obtain 3D sub-diffraction-limited information about rotational symmetric structures.

Electronic skins are essential for real-time health monitoring and tactile perception in robots. Although the use of soft elastomers and microstructures have improved the sensitivity and pressure-sensing range of tactile sensors, the intrinsic viscoelasticity of soft polymeric materials remains a long-standing challenge resulting in cyclic hysteresis. This causes sensor data variations between contact events that negatively impact the accuracy and reliability. Here, we introduce the Tactile Resistive Annularly Cracked E-Skin (TRACE) sensor to address the inherent trade-off between sensitivity and hysteresis in tactile sensors when using soft materials. We discovered that piezoresistive sensors made using an array of three-dimensional (3D) metallic annular cracks on polymeric microstructures possess high sensitivities (> 10⁷ Ω · kPa−1), low hysteresis (2.99 ± 1.37%) over a wide pressure range (0–20 kPa), and fast response (400 Hz). We demonstrate that TRACE sensors can accurately detect and measure the pulse wave velocity (PWV) when skin mounted. Moreover, we show that these tactile sensors when arrayed enabled fast reliable one-touch surface texture classification with neuromorphic encoding and deep learning algorithms.

Despite rapid progress in UAV-based infrared vehicle detection, achieving reliable target recognition remains challenging due to dynamic viewpoint variations and platform instability. The inherent limitations of infrared imaging, particularly low contrast ratios and thermal crossover effects, significantly compromise detection accuracy. Moreover, the computational constraints of edge computing platforms pose a fundamental challenge in balancing real-time processing requirements with detection performance. Here, we present MonoSeg, a novel instance segmentation framework optimized for UAV perspective infrared vehicle detection. Our approach introduces three key innovations: (1) the Ghost Feature Bottle Cross module (GFBC), which enhances backbone feature extraction efficiency while significantly reducing computational over-head; (2) the Scale Feature Recombination module (SFR), which optimizes feature selection in the Neck stage through adaptive multi-scale fusion; and (3) Comprehensive Loss function that enforces precise instance boundary delineation. Extensive experimental evaluation on bench-mark datasets demonstrates that MonoSeg achieves state-of-the-art performance across standard metrics, including Box mAP and Mask mAP, while maintaining substantially lower computational requirements compared to existing methods.

Commercial aircraft fault diagnosis is an important means to ensure the reliability and safety of commercial aircraft. Traditional knowledge-driven and data-driven fault diagnosis methods lack interpretability in engineering mechanisms, making them difficult to promote and apply. To address the issue of lack of interpretability, this paper conducts a fault knowledge graph for commercial aircraft fault diagnosis, using the fault logic in the logic diagram to increase the interpretability of diagnostic work. Firstly, to avoid the inefficiency of logic diagram applications, an executable logic diagram model is established, which can perform mathematical analysis and achieve fault diagnosis and localization using operational data as input. Then, the logic diagram is sorted out to obtain the hidden fault knowledge in the logic diagram, which is used to construct a fault knowledge graph to help achieve cause localization and rapid troubleshooting. The methods proposed in this paper are all validated through case studies of abnormal low-pressure faults in domestic commercial aircraft hydraulic systems. The results show that the logic diagram model can perform model simulation and fault diagnosis based on operational data, and the fault knowledge graph can quickly locate abnormal monitoring parameters and guide troubleshooting work based on existing information.

Mapping the spatial distribution of interaction sites between FG nucleoporins and nuclear transport receptors within the native NPC through SPEED microscopy reveals the 3D configuration of the FG-nucleoporin barrier and competition among transport receptors. A selective barrier formed by intrinsically disordered Phe-Gly (FG) nucleoporins (Nups) allows transport receptor (TR)-facilitated translocation of signal-dependent cargos through the nuclear pore complexes (NPCs) of eukaryotic cells. However, the configuration of the FG-Nup barrier and its interactions with multiple TRs in native NPCs remain obscure. Here, we mapped the interaction sites of various TRs or FG segments within the FG-Nup barrier by using high-speed super-resolution microscopy and used these sites to reconstruct the three-dimensional tomography of the native barrier in the NPC. We found that each TR possesses a unique interaction zone within the FG-Nup barrier and that two major TRs, importin β1 and Crm1, outcompete other TRs in binding FG Nups. Moreover, TRs may alter the tomography of the FG-Nup barrier and affect one another's pathways under circumstances of heavy competition.

The nuclear pore complex (NPC), the sole gateway for nucleocytoplasmic exchange in eukaryotic cells, allows for the passive diffusion of small molecules and transport-receptor-facilitated translocation of signal-dependent cargo molecules. Whether small molecules passively diffuse through a single central channel or through multiple holes of a hydrogel network is a subject of debate. Additionally, whether the passive and facilitated transport systems occupy distinct or overlapping physical regions of the NPC remains unclear. Here, we directly test these models using three-dimensional super-resolution fluorescence microscopy of human cells. This approach reveals that a single viscous central channel in the NPC acts as the sole pathway for passive diffusion of various small molecules; transport receptors and their cargo complexes take distinct transport routes in the periphery, which is occluded by phenylalanine-glycine filaments. Furthermore, the passive and facilitated passageways in the NPC are closely correlated, and their conformations can be simultaneously regulated by Importin β1 (a major transport receptor) and RanGTP (a critical regulator of transport directionality). These results strongly favor a self-regulated viscous channel configuration in native NPCs over the porous hydrogel meshwork model.

The development of artificial intelligence and the Internet of things has motivated extensive research on self-powered flexible sensors. The conventional sensor must be powered by a battery device, while innovative self-powered sensors can provide power for the sensing device. Self-powered flexible sensors can have higher mobility, wider distribution, and even wireless operation, while solving the problem of the limited life of the battery so that it can be continuously operated and widely utilized. In recent years, the studies on piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs) have mainly concentrated on self-powered flexible sensors. Self-powered flexible sensors based on PENGs and TENGs have been reported as sensing devices in many application fields, such as human health monitoring, environmental monitoring, wearable devices, electronic skin, human–machine interfaces, robots, and intelligent transportation and cities. This review summarizes the development process of the sensor in terms of material design and structural optimization, as well as introduces its frontier applications in related fields. We also look forward to the development prospects and future of self-powered flexible sensors.

Acute lung injury (ALI) is a life-threatening disease that has received considerable critical attention in the field of intensive care. This study aimed to explore the role and mechanism of vitamin K2 (VK2) in ALI. Intraperitoneal injection of 7 mg/kg LPS was used to induce ALI in mice, and VK2 injection was intragastrically administered with the dose of 0.2 and 15 mg/kg. We found that VK2 improved the pulmonary pathology, reduced myeloperoxidase (MPO) activity and levels of TNF-α and IL-6, and boosted the level of IL-10 of mice with ALI. Moreover, VK2 played a significant part in apoptosis by downregulating and upregulating Caspase-3 and Bcl-2 expressions, respectively. As for further mechanism exploration, we found that VK2 inhibited P38 MAPK signaling. Our results also showed that VK2 inhibited ferroptosis, which manifested by reducing malondialdehyde (MDA) and iron levels, increasing glutathione (GSH) level, and upregulated and downregulated glutathione peroxidase 4 (GPX4) and heme oxygenase-1 (HO-1) expressions, respectively. In addition, VK2 also inhibited elastin degradation by reducing levels of uncarboxylated matrix Gla protein (uc-MGP) and desmosine (DES). Overall, VK2 robustly alleviated ALI by inhibiting LPS-induced inflammation, apoptosis, ferroptosis, and elastin degradation, making it a potential novel therapeutic candidate for ALI.