Human vital signs such as the heartbeat and respiration are important physiological parameters for public health care. Precisely monitoring these very minute and complex time-dependent signals in a simple, low-cost way is still a challenge. This study shows a novel fabrication of vertically well-aligned piezoelectric nanowire arrays with preferential polarization orientation as highly sensitive self-powered sensors for monitoring vital signs. The process realizes in situ poling of the P(VDF-TrFE) nanowires within the nanopores of the anodized aluminium oxide (AAO) template to yield a preferential alignment of both nanowires and the polymer chains required for superior sensitivity in one step. The resulting self-powered flexible sensor shows high sensitivity, good stability and strong power-generating performance. Under bending conditions, the device exhibits a maximum voltage of ∼4.8 V and a current density of ∼0.11 μA cm⁻². The fabricated self-powered sensor shows a linear relationship of output voltage versus compressive force with a high sensitivity, and the piezoelectric voltage of the P(VDF-TrFE) nanowire array is enhanced 9 times that of conventional spin-coated bulk films. Furthermore, the highly sensitive vertically well-aligned nanowire array can be applied as a self-powered sensor for detecting some tiny human activities including breath, heartbeat pulse, and finger movements, which may possibly serve for medical diagnostics as sensors, robotics and smart electronic devices.

Fabric defect detection is crucially essential for maintaining fabric quality. Existing intelligent detection systems, however, often fall short in real scenarios due to significant variations in defect sizes, which typically manifest as small and elongated structures. In addition, insufficient samples in real scenarios result in a long-tail distribution in training data, posing challenges to the balanced data prerequisites of having collecting balanced data for deep learning methods. This article introduces the joint dynamic contexts and balanced learning (JDCBL) framework to cope with these challenges. Within this framework, contextual transformer block (CoTBlock) is designed to capture both global and fine-grained features of fabric defects simultaneously, which forms contextual transformer network (CoTNet). This modification boosts the interactivity of contextual information between input keys and effectively enhances self-attention learning. Additionally, class average and class complement strategies balanced contrastive loss (BCL) amplify the exposure of tail-class defects so they can be accurately identified. Extensive validation on the Tianchi fabric dataset demonstrates that the JDCBL framework surpasses other leading methods in multiple defect detection tasks, achieving a MAP50 of 39.3%—over 5% higher than competing methods.

Biological hydrogels are widely available in terms of raw material sources and can be processed and molded using relatively simple techniques. Hydrogels can offer abundant three-dimensional, water-containing channels that facilitate the reaction between gases and dye, making them the preferred choice for the solid support layer in colorimetric indicators. However, biomass hydrogels exhibit inferior mechanical properties, making them unsuitable for large-scale manufacturing processes. In this study, four dual-network composite hydrogels Agar/Gelatin, Sodium Alginate/Agar, Sodium Alginate/Poly (vinyl alcohol), Sodium Alginate/Gelatin (AG/Gel, SA/AG, SA/PVA and SA/Gel) prepared through hybrid strategies. Furthermore, the influence of the dual-network structure on the mechanical properties and ammonia response was systematically investigated, using microscopy and Fourier transform infrared spectroscopy (FTIR) characterization method. The experimental results demonstrate that the incorporation of SA into original hydrogel matrices can significantly enhance both the mechanical and ammonia response performance due to the secondary topological network structure. The interpenetrating double network structure was effectively regulated through the calcium ion cross-linking process. The color difference threshold of SA/PVA’s response to ammonia gas is 10, it holds promise for rapid detection applications. The SA/Gel composite hydrogel exhibits excellent mechanical robustness and toughness. The tensile strength of the SA/Gel sample is 11 times that of a single gel, and the toughness is 80 times greater, suggesting its suitability for large-scale manufacturing of colorimetric indicator.

Industrial Quality Inspection (IQI) is a pivotal part of intelligent manufacturing, critical to ensuring product quality. Deep learning-based methods have attracted growing attention for their excellent feature extraction ability, outperforming traditional detection approaches. However, existing methods still face issues of insufficient efficiency and poor transferability, and this paper proposes a Fine-tuned Lightweight Faster RCNN (FLF-RCNN) framework designed to address key challenges in IQI, including the trade-off between accuracy and computational efficiency, and the insufficient adaptability of preset anchor box ratios. FLF-RCNN introduces a lightweight backbone network, LSNet, which enhances the receptive field through architectural optimization. Specifically, it uses a collaborative mechanism that combines large kernel convolutions for extracting contextual information and small kernel convolutions for capturing fine-grained details. This mechanism enables the model to efficiently and precisely represent defects. To enhance generalization in data-scarce industrial scenarios, the framework leverages transfer learning with pretrained weights. Furthermore, an Adaptive Anchor Box-Adjustment Module (AAB-AM) based on K-means clustering is introduced to improve detection across varied defect scales. Extensive experiments conducted on the Tianchi dataset show that FLF-RCNN achieves a mAP50 of 43.6%, outperforming detectors using MobileNet and EfficientNet backbones and surpassing the baseline Faster R-CNN by 7.9% in mAP50. Meanwhile, the proposed method reduces computational complexity by approximately 40%, reaching 98.65 GFLOPs, and decreases parameter count by around 30% to 28.2M. These results demonstrate that FLF-RCNN offers a feasibility and practical solution for IQI, achieving a superior accuracy-efficiency balance within the two-stage detection paradigm.