Search Results (4)

The low fluorescence quantum efficiency of hydrophilic modified spiropyran in hydrogel matrices cannot be naturally improved during photoresponsive operation, which significantly limits their practical applications.In this study, a hybrid hydrogel system integrating metal plasmon resonance-enhanced fluorescence effects is designed through copolymerization of N,N′-bis(acryloyl)cystamine-modified Au nanoparticles (Au NPs), hydrophilic graft-modified spiropyran molecules, and N-isopropylacrylamide. This approach successfully achieves a spiropyran-based fluorescent hydrogel sensor with enhanced fluorescence intensity. Furthermore, an inverted pyramid-structured surface is engineered on the hydrogel using a template-assisted strategy, combining anti-reflection optical effects with plasmonic enhancement mechanisms. Molecular modification facilitated the integration of spiropyran and Au NPs into the hydrogel molecular chains, enhancing the dispersion of Au NPs within the hydrogel matrix and preventing fluorescence quenching from direct contact between Au NPs and spiropyran. Additionally, the anti-reflection effect of the hydrogel surface microstructure and the plasmon resonance effect of Au NPs were crucial in boosting the sensor’s fluorescence. Finally, the fluorescence intensity of the hydrogel increased by 10.2 times. In addition, under the action of excitation light, this sensor exhibited dual responsiveness of colorimetry and fluorescence, allowing for the sensing of heavy metal ions. The limit of detection for Zn²⁺ is as low as 0.803 μM, and the hydrogel exhibited more than 10 cycles of photo-isomerization and ion responsiveness. Full article

As a critical task in underground coal mining, personnel identification and positioning in fully mechanized mining faces are essential for safety. Yet, complex environmental factors—such as narrow tunnels, heavy dust, and uneven lighting—pose significant challenges to accurate detection. In this paper, we propose a personnel detection network, MSS-YOLO, for fully mechanized mining faces based on YOLOv8. By designing a Multi-Scale Edge Enhancement (MSEE) module and fusing it with the C2f module, the performance of the network for personnel feature extraction under high-dust or long-distance conditions is effectively enhanced. Meanwhile, by designing a Spatial Pyramid Shared Conv (SPSC) module, the redundancy of the model is reduced, which effectively compensates for the problem of the max pooling being prone to losing the characteristics of the personnel at long distances. Finally, the lightweight Shared Convolutional Detection Head (SCDH) ensures real-time detection under limited computational resources. The experimental results show that compared to Faster-RCNN, SSD, YOLOv5s6, YOLOv7-tiny, YOLOv8n, and YOLOv11n, MSS-YOLO achieves AP50 improvements of 4.464%, 10.484%, 3.751%, 4.433%, 3.655%, and 2.188%, respectively, while reducing the inference time by 50.4 ms, 11.9 ms, 3.7 ms, 2.0 ms, 1.2 ms, and 2.3 ms. In addition, MSS-YOLO is combined with the SGBM binocular stereo vision matching algorithm to provide a personnel 3D spatial position solution by using disparity results. The personnel location results show that in the measurement range of 10 m, the position errors in the x-, y-, and z-directions are within 0.170 m, 0.160 m, and 0.200 m, respectively, which proves that MSS-YOLO is able to accurately detect underground personnel in real time and can meet the underground personnel detection and localization requirements. The current limitations lie in the reliance on a calibrated binocular camera and the performance degradation beyond 15 m. Future work will focus on multi-sensor fusion and adaptive distance scaling to enhance practical deployment. Full article

Automatic ship detection is a crucial task within the domain of maritime transportation management. With the progressive success of convolutional neural networks (CNNs), a number of advanced CNN models have been presented in order to detect ships. Although these detection models have achieved marked performance, several undesired results may occur under complex maritime conditions, such as missed detections, false positives, and low detection accuracy. Moreover, the existing detection models endure large number of parameters and heavy computation cost. To deal with these problems, we suggest a lightweight ship model of detection called DSSM–LightNet based upon the improved YOLOv8n. First, we introduce a lightweight Dual Convolutional (DualConv) into the model to lower both the number of parameters and the computational complexity. The principle is that DualConv combines two types of convolution kernels, 3x3 and 1x1, and utilizes group convolution techniques to effectively reduce computational costs while processing the same input feature map channels. Second, we propose a Slim-neck structure in the neck network, which introduces GSConv and VoVGSCSP modules to construct an efficient feature-fusion layer. This fusion strategy helps the model better capture the features of targets of different sizes. Meanwhile, a spatially enhanced attention module (SEAM) is leveraged to integrate with a Feature Pyramid Network (FPN) and the Slim-neck to achieve simple yet effective feature extraction, minimizing information loss during feature fusion. CIoU may not accurately reflect the relative positional relationship between bounding boxes in some complex scenarios. In contrast, MPDIoU can provide more accurate positional information in bounding-box regression by directly minimizing point distance and considering comprehensive loss. Therefore, we utilize the minimum point distance IoU (MPDIoU) rather than the Complete Intersection over Union (CIoU) Loss to further enhance the detection precision of the suggested model. Comprehensive tests carried out on the publicly accessible SeaShips dataset have demonstrated that our model greatly exceeds other algorithms in relation to their detection accuracy and efficiency, while reserving its lightweight nature. Full article

Pyramidal systems refer to a particular type of resistance training in which sets are performed with increasing (or decreasing) weight, in such a way that the number of repetitions is low when the weight is high (and vice versa). Multiple implementations exist such as the light-to-heavy, triangle or asymmetric triangle system. They are similar to traditional training, but with slightly different impact on training volume, endurance or power outcome. Therefore, pyramidal systems are ideal candidates for practitioners willing to tune their training routine. Full article