Abstract:To meet the growing market demand for low-cost supercapacitor electrode materials,the used zongzi packaging leaves were used as a precursor,and a simple one-step KHCO3 activation method was employed to prepare N/O/Si self-doped hierarchical porous carbon under a temperature of 700℃and with a small amount of activator(mass ratio of KHCO3 to zongzi packaging leaves was 1∶1).The symmetric supercapacitor device was assembled by the prepared porous carbon.The microstructure of this material was analyzed and its electrochemical performance was tested.The results showed that this was attributed to the developed pore structure of the prepared porous carbon and the co-doping of N,O,and Si heteroatoms,the symmetric supercapacitor exhibited a specific capacitance of 142.4 F/g at a current density of 0.1 A/g.The specific capacitance retention rate reached 85.94%after 10000 cycles of charge-discharge at 2.0 A/g,demonstrating excellent cycle performance.The work provided the new idea for the comprehensive reuse of used zongzi packaging leaves and the preparation and application of porous carbon doped by multiple hetero elements.

Abstract:To address the issue that the slight differences among the tiny defects(such as missing holes,rat bites,open circuits,etc.)occurring during the manufacturing of printed circuit board(PCB)made it difficult to distinguish them,the hierarchical partial multi-scale convolution and small targets strengthen pyramid-you only look once(HS-YOLO)model based on YOLO version 11 nano(YOLOv11n)was proposed.Firstly,a hierarchical partial multi-scale convolution(HPMSConv)module was introduced,which adopted a progressive mixed feature fusion strategy to enhance the model's adaptability to various defect types.Secondly,a small target enhancement pyramid was proposed,in which a crossover all nucleus module was designed,and improvements were made to the path aggregation feature pyramid network(PAFPN).By focusing on the global information of defects,the detection capability for small targets was significantly improved.The experimental results on the dataset of the Peking University market powered by printed circuit board(PKU-Market-PCB)showed that the mean average precision of the HS-YOLO model under the intersection over union thresholds ranging from 0.50 to 0.95 with a step size of 0.05 was increased by 3.7 percent compared with that of the YOLOv11n model,and the recall rate was increased by 4.1 percent.The HS-YOLO model not only enhanced the detection accuracy of minute PCB defects but also effectively addressed the issues of low differentiation and small target detection capability in PCB defect detection,providing a high-performance solution for automated PCB inspection.

Abstract:To investigate the characteristics of forest wellness tourist space suitability distribution and the spatial suitability at the administrative scale in the Wuling Mountain Area,a suitability evaluation system of forest wellness tourist space in the Wuling Mountain Area was constructed based on three dimensions,namely,natural conditions,ecological quality,and location conditions,which combined literature analysis,the analytic hierarchy process,and geographic information systems(GIS)to perform quantitative evaluations and visualizations.The research showed that the suitable and the most suitable areas accounted for 57.805%of the Wuling Mountain Area.The administrative regions were categorized into four types:foundational cultivation zones,selective cultivation zones,stable development zones,and core development zones,according to the composite scores of forest wellness tourist space suitability.The development strategies of different types of regions were different.The research could provide decision support for promoting forest wellness tourist space suitability level and high-quality development of tourism industry.

Abstract:To address the issues of weakened target information and insufficiently enriched details in infrared-visible image fusion,and to enhance the semantic information retention capability of fused images,a semantic segmentation-skip connections for infrared-visible image fusion(SS-SC-IVIF)adversarial learning model was proposed.At the generator end,the mask with semantic information was obtained through semantic segmentation,and the image was divided into the infrared image target area and the visible light image background area;the two source images and the result of them were used to extract more source image feature information;and the semantic information mask was designed to guide the feature extraction and reconstruction.The two discriminators prompted the fusion results to retain the intensity information of the infrared image and the texture information of the visible image.The results showed that in six objective evaluation indicators except standard deviation(SD),the other five indicators in Netherlands organization for applied scientific research(TNO)data sets and road data sets were improved,of which the entropy(EN)indicator of SS-SC-IVIF model improved by 3.7%and the structural similarity(SSIM)indicator improved by 4.3%compared with the average of the comparison models.The detail information of the source images was maximally retained,and the fusion accuracy was significantly improved by the model.

Abstract:To address the issue that shared cross-domain recommendation models fail to effectively capture and transfer cross-domain information in data-sparse scenarios,leading to insufficient user preference transfer and decreased recommendation efficiency,a cross-domain recommendation model based on graph neural network and multi-head attention mechanism(GMACDR)was proposed.Firstly,the node to vector(Node2Vec)algorithm was leveraged for graph embedding,where node propagation paths were constructed through random walks,and high-fidelity node representations were learned using the Skip-gram model.Secondly,during the information propagation process within the graph convolutional network,a multi-head attention mechanism was incorporated to dynamically modulate the propagation weights of cross-domain information,effectively capturing intricate user-item interactions.Finally,a dynamic weighting mechanism was employed to aggregate domain-specific features in a weighted manner,refining the feature fusion process and generating more expressive and representative user embeddings.Compared to the best baseline model,the hit rate increased by 4.61%,the normalized discounted cumulative gain increased by 10.47%,and the mean reciprocal rank increased by 9.71%.The research results demonstrated that the model provided more accurate recommendations for users.

Abstract:To improve the information of agricultural alien plants in Enshi Prefecture,Hubei Province,the species composition,spatial distribution patterns,and environmental driving factors of agricultural alien plants in this region were analyzed by using field surveys and literature reviews.The results indicated that 134 species of agricultural alien plants existed in Enshi Prefecture,belonging to 89 genera of 36 families,with Asteraceae,Amaranthaceae,and Fabaceae being the dominant families.Among them,herbaceous plants were predominant,and annual herbaceous plants accounted for 47.0%.The analysis revealed that these plants were primarily driven by climatic conditions and anthropogenic introductions,exhibiting strong aggregation in gardens and cultivated land,which might lead to a decline in native plant species and a decrease in biodiversity in this region,posing a threat to the ecosystem's stability.This study improved the agricultural alien plants list and spatial distribution database in Enshi Prefecture,providing an important reference for ecological security risk prevention in this region.

Abstract:To achieve accurate detection of the coal flow and deviation of belt conveyor,an improved proportional-integral-derivative network(PIDNet)semantic segmentation model was proposed.Firstly,the feature stabilization(FS)module was introduced to dynamically assign higher weights to critical features,reducing information loss caused by continuous downsampling in the network.Secondly,the connection architecture of the parallel aggregation pyramid pooling(PAPP)module was reconstructed,where standard convolutions were replaced with deformable convolution network(DCN),and a lightweight efficient channel attention(ECA)mechanism was embedded.This ensured the integrity of multi-scale features while reducing model parameters count.Finally,the dual-path edge enhancement(DPEE)module was designed,where edge detection and semantic segmentation paths were collaboratively optimized,improving the intersection over union of edge detection.The results demonstrated that the improved PIDNet model achieved a detection speed of 78 frames/s,with the mean intersection over union and mean accuracy of segmentation targets reaching 86.51%and 95.38%,respectively.Compared to the original PIDNet model,the proposed model achieved improvements of 1.79 and 2.21 percentage points.This study provided effective technical support for coal flow intelligent monitoring of belt conveyors.

Abstract:To enhance the semantic segmentation accuracy of safety helmets in complex scenarios and address issues involving blurred edges,poor segmentation of small objects,and multi-scale variations,a multi-scale attention pyramid scene parsing network(MA-PSPNet)model for construction site scenes integrated with attention mechanism and edge guidance was developed.In the proposed architecture,a multi-scale convolutional attention(MSCA)module was embedded within the feature extraction backbone network of the model to enhance feature representation in critical regions.An edge-guided attention(EGA)module was incorporated subsequently to the second-stage feature extraction backbone network to refine boundary identification capabilities.Furthermore,the pyramid pooling structure of the pyramid scene parsing network(PSPNet)was replaced by an atrous spatial pyramid pooling module to strengthen multi-scale adaptation.The results showed that the mean intersection over union of MA-PSPNet model was 83.28%,with an improvement of 9.13 percentage points compared to the original PSPNet model.The pixel accuracy and mean pixel accuracy were quantified at 95.62%and 88.74%,respectively.MA-PSPNet model could enhance effectively safety helmet segmentation precision and boundary awareness within complex industrial environments and had good practicality.

Abstract:An antioxidant system exists in plants and it maintains the dynamic equilibrium of ROS nor?mally.The adversity stress is the main reason for imbalance of ROS and decreased antioxidation. Under stress conditions, ROS excessive accumulation causes the insufficient ability of antioxidant systems on scavenging ROS relatively,and the oxidative damage to plants.This review makes an analysis and summary on the effects of accumulated ROS under the adversity stress,focusing on the dynamic response of enzy?matic antioxidant systems and secondary metabolites to the adversity stress including salt,ultraviolet, high temperature stress and so on.