Explore the vast range of titles available.

The use of metal-free carbon nitride and light to drive catalytic transformations constitutes a sustainable strategy for organic synthesis. At the moment, enhancing the intrinsic activity of CN catalysts by tuning the interfacial coupling between catalyst and substrate remains challenging. Herein, we demonstrate that urea-derived carbon nitride catalysts with the abundant −NH 2 groups and the relative positive charged surface could effectively complex with the deprotonated anionic intermediate to improve the adsorption of organic reactants on the catalyst surface. The decreased oxidation potential and upshift in its highest occupied molecular orbital position make the electron abstraction kinetics by the catalyst more energetically favorable. The prepared catalyst is thus utilized for the photocatalytic cyclization of nitrogen-centered radicals for the synthesis of diverse pharmaceutical-related compounds (33 examples) with high activity and reusability, which shows competent performance to the homogeneous catalysts. Carbon nitride catalysts with positively charged surfaces and abundant −NH2 are found to be effective photocatalysts for dihydropyrazole synthesis. A surface-mediated mechanism where deprotonated intermediates interact with the surface is proposed.

With lightweight, multifunctional, and designable characteristics, porous/lattice structures have started to be used in aerospace applications. Porous/lattice structures applied in the thermal management technology of aerospace vehicles have attracted much attention. In the past few years, many related numerical and experimental investigations on flow, heat transfer, modelling methodology, and manufacturing technology of porous/lattice structures applied in thermal management systems have been widely conducted. This paper lists the investigations and applications of porous/lattice structures applied in thermal management technology from two aspects, i.e., heat transfer enhancement by porous/lattice structures and transpiration cooling. In addition, future developments and challenges based on the previous investigations are analyzed and summarized. With the higher requirements of thermal protection for aerospace applications in the future, thermal management technology based on porous/lattice structures shows good prospects.

Tapping line detection and rubber tapping pose estimation are challenging tasks in rubber plantation environments for rubber tapping robots. This study proposed a method for tapping line detection and rubber tapping pose estimation based on improved YOLOv8 and RGB-D information fusion. Firstly, YOLOv8n was improved by introducing the CFB module into the backbone, adding an output layer into the neck, fusing the EMA attention mechanism into the neck, and modifying the loss function as NWD to realize multi-object detection and segmentation. Secondly, the trunk skeleton line was extracted by combining level set and ellipse fitting. Then, the new tapping line was located by combining edge detection and geometric analysis. Finally, the rubber tapping pose was estimated based on the trunk skeleton line and the new tapping line. The detection results from 597 test images showed the improved YOLOv8n’s detection mAP0.5, segmentation mAP0.5, and model size were 81.9%, 72.9%, and 6.06 MB, respectively. The improved YOLOv8n’s effect and efficiency were superior compared to other networks, and it could better detect and segment natural rubber tree image targets in different scenes. The pose estimation results from 300 new tapping lines showed the average success rate and average time consumed for rubber tapping pose estimation were 96% and 0.2818 s, respectively. The positioning errors in x, y, and z directions were 0.69 ± 0.51 mm, 0.73 ± 0.4 mm, and 1.07 ± 0.56 mm, respectively. The error angles in a, o, and n directions were 1.65° ± 0.68°, 2.53° ± 0.88°, and 2.26° ± 0.89°, respectively. Therefore, this method offers an effective solution for rubber tapping pose estimation and provides theoretical support for the development of rubber tapping robots.

In this study, to investigate the influencing factors between the rubber tapping knife and the cutting surface quality during the tapping process of natural rubber trees, as well as the cutting quality of wood using horizontal push-type knives, a simulated rubber tapping experiment was conducted, specifically involving the use of a rubber tapping knife to cut rubber wood blocks. During the experiment, the V-angle of the rubber knife and the feed speed were varied, and the variations in cutting force and cutting distance were monitored in real time. The surface flatness of the processed rubber wood blocks was then tested, followed by a latex flow experiment. After the experiments, the wear condition of the cutter was measured, and an analysis was performed to evaluate the relationship between five evaluation indices: maximum cutting force, energy consumption, chip morphology, cutting surface quality, and latex flow velocity, with the V-angle of the rubber tapping knife and the feed speed. The test results showed that the 83° knife achieved the best cutting surface quality, while the 90° knife had the least wear. Based on the comprehensive evaluation of cutting performance, the 83° knife with a tapping speed of 90 mm/min produced in the best overall evaluation of cutting surface quality and knife wear.

Natural rubber is an essential raw material for industrial products and plays an important role in social development. A variety of diseases can affect the growth of rubber trees, reducing the production and quality of natural rubber. Therefore, it is of great significance to automatically identify rubber leaf disease. However, in practice, different diseases have complex morphological characteristics of spots and symptoms at different stages and scales, and there are subtle interclass differences and large intraclass variation between the symptoms of diseases. To tackle these challenges, a group multi-scale attention network (GMA-Net) was proposed for rubber leaf disease image recognition. The key idea of our method is to develop a group multi-scale dilated convolution (GMDC) module for multi-scale feature extraction as well as a cross-scale attention feature fusion (CAFF) module for multi-scale attention feature fusion. Specifically, the model uses a group convolution structure to reduce model parameters and provide multiple branches and then embeds multiple dilated convolutions to improve the model’s adaptability to the scale variability of disease spots. Furthermore, the CAFF module is further designed to drive the network to learn the attentional features of multi-scale diseases and strengthen the disease features fusion at different scales. In this article, a dataset of rubber leaf diseases was constructed, including 2,788 images of four rubber leaf diseases and healthy leaves. Experimental results show that the accuracy of the model is 98.06%, which was better than other state-of-the-art approaches. Moreover, the model parameters of GMA-Net are only 0.65 M, and the model size is only 5.62 MB. Compared with MobileNetV1, V2, and ShuffleNetV1, V2 lightweight models, the model parameters and size are reduced by more than half, but the recognition accuracy is also improved by 3.86–6.1%. In addition, to verify the robustness of this model, we have also verified it on the PlantVillage public dataset. The experimental results show that the recognition accuracy of our proposed model is 99.43% on the PlantVillage dataset, which is also better than other state-of-the-art approaches. The effectiveness of the proposed method is verified, and it can be used for plant disease recognition.

Natural rubber harvesting remains highly dependent on manual labor, particularly during latex cup collection, which limits efficiency and increases operational costs. Intelligent robotic harvesting systems require accurate visual perception and reliable grasp point positioning under rubber plantation environments. However, latex cups are typically small, visually diverse, and often affected by adjacent latex drains, making detection and manipulation challenging for existing vision models. To address these challenges, this study proposed a lightweight vision-based framework for latex cup detection and grasp-point positioning in automated rubber harvesting. The proposed NRLC-YOLO is developed based on YOLO11n-seg by integrating a lightweight backbone, a local–global attention enhancement module, and a dynamic convolution strategy. In addition, a fast grasp-point positioning method is designed to determine the gripping center of latex cups, enabling stable robotic manipulation. As validated by experimental results, our model yields a mAP @50–95 of 78.5%, which surpasses the baseline approach with fewer parameters and lower computational costs. The average error of the proposed grasp-point positioning scheme is measured at 8.08 pixels. Field experiments further demonstrate a grasping success rate of up to 93.3% under real plantation conditions. The proposed framework provides an efficient AI-enabled perception solution for automated latex cup harvesting and offers practical support for the development of intelligent rubber plantation management systems.

Objectives To compare the diagnostic value of high frame rate contrast-enhanced ultrasound (H-CEUS) in distinguishing gallbladder adenomas from cholesterol polyp lesions with that of CEUS. Methods This study enrolled 94 patients with gallbladder polyp lesions (GPLs) who underwent laparoscopic cholecystectomy. CEUS and H-CEUS were performed before surgery. The perfusion features of GPLs and the final diagnosis as determined by both technologies were compared. Results There were differences in vascular types between gallbladder adenomas and cholesterol polyp lesions observed on H-CEUS ( p < 0.05), while there were no differences in vascular types between gallbladder adenomas and cholesterol polyp lesions observed on CEUS ( p > 0.05). In the cholesterol polyp lesion group, there were no differences in vascular types between CEUS and H-CEUS ( p > 0.05), while the vascular types were different between CEUS and H-CEUS in the gallbladder adenoma group ( p < 0.05). The diagnostic value of H-CEUS in distinguishing gallbladder adenomas from cholesterol polyp lesions was better than that of CEUS. Conclusions H-CEUS improved the time resolution by increasing the frame rate, which helped to accurately reflect the difference in the microcirculation of GPLs and improved the ability of a differential diagnosis between cholesterol polyp lesions and adenomas. H-CUES may provide an effective means of imaging for patients with GPLs regarding the choice of treatment options. Key Points • High frame rate CEUS improves the time resolution of CEUS by increasing the frame rate. • High frame rate CEUS is helpful to accurately evaluate the microvascular morphology of a gallbladder polyp lesion in the arterial phase. • High frame rate CEUS helps patients with gallbladder polyp lesions to choose the appropriate treatment means.

This paper proposes a technique for precise distance measurement under laser random noise using autocorrelation demodulation. We conducted measurements within a 2 m range using a commercial digitizer with a sampling rate of 40 GS/s. The single signal acquisition time is 40 μs, and the measurement results achieved an accuracy of 30 μm and a standard deviation (SD) of 8 μm. This method can also improve measurement accuracy by increasing the sampling rate and expanding the measurement range by extending the duration of a single sampling.

Aiming at the problem that the rubber tapping robot finds it difficult to accurately detect the tapped area and locate the new tapping line for natural rubber trees due to the influence of the rubber plantation environment during the rubber tapping operation, this study proposes a method for detecting the tapped area and locating the new tapping line for natural rubber trees based on the improved mask region convolutional neural network (Mask RCNN). First, Mask RCNN was improved by fusing the attention mechanism into the ResNeXt, modifying the anchor box parameters, and adding a tiny fully connected layer branch into the mask branch to realize the detection and rough segmentation of the tapped area. Then, the fine segmentation of the existing tapping line was realized by combining edge detection and logic operation. Finally, the existing tapping line was moved down a certain distance along the center line direction of the left and right edge lines of the tapped area to obtain the new tapping line. The tapped area detection results of 560 test images showed that the detection accuracy, segmentation accuracy, detection average precision, segmentation average precision, and intersection over union values of the improved Mask RCNN were 98.23%, 99.52%, 99.6%, 99.78%, and 93.71%, respectively. Compared with other state-of-the-art approaches, the improved Mask RCNN had better detection and segmentation performance, which could better detect and segment the tapped area of natural rubber trees under different shooting conditions. The location results of 560 new tapping lines under different shooting conditions showed that the average location success rate of new tapping lines was 90% and the average location time was 0.189 s. The average values of the location errors in the x and y directions were 3 and 2.8 pixels, respectively, and the average value of the total location error was 4.5 pixels. This research not only provides a location method for the new tapping line for the rubber tapping robot but also provides theoretical support for the realization of rubber tapping mechanization and automation.

In the original publication [...]