Unmanned Aerial Vehicle (UAV) technology is a promising solution for providing high-quality mobile services (e.g., edge computing and local caching) to ground users. How to dynamically determine a UAV swarm's cooperative path planning to best meet many users' spatio-temporally distributed demands is an important question but unaddressed in the literature. Regarding a single UAV's path planning design, we manage to substantially simplify the traditional dynamic program and propose an optimal algorithm of low computation complexity. After coordinating a large number K of UAVs, this simplified dynamic optimization problem becomes intractable and we alternatively present a fast iterative cooperation algorithm with provable approximation ratio $1-(1-\frac{1}{K})^{K}$ in the worst case. To relax UAVs' battery capacity limit for sustainable service provisioning, we further allow UAVs to travel to charging stations in the mean time and thus jointly design UAVs' path planning over users' locations and charging stations. We successfully transform the problem to an integer linear program by creating novel directed acyclic graph of the UAV-state transition diagram, and propose an iterative algorithm with constant approximation ratio.

Due to the existence of cotton weeds in a complex cotton field environment with many different species, dense distribution, partial occlusion, and small target phenomena, the use of the YOLO algorithm is prone to problems such as low detection accuracy, serious misdetection, etc. In this study, we propose a YOLOv8-DMAS model for the detection of cotton weeds in complex environments based on the YOLOv8 detection algorithm. To enhance the ability of the model to capture multi-scale features of different weeds, all the BottleNeck are replaced by the Dilation-wise Residual Module (DWR) in the C2f network, and the Multi-Scale module (MSBlock) is added in the last layer of the backbone. Additionally, a small-target detection layer is added to the head structure to avoid the omission of small-target weed detection, and the Adaptively Spatial Feature Fusion mechanism (ASFF) is used to improve the detection head to solve the spatial inconsistency problem of feature fusion. Finally, the original Non-maximum suppression (NMS) method is replaced with SoftNMS to improve the accuracy under dense weed detection. In comparison to YOLO v8s, the experimental results show that the improved YOLOv8-DMAS improves accuracy, recall, mAP0.5, and mAP0.5:0.95 by 1.7%, 3.8%, 2.1%, and 3.7%, respectively. Furthermore, compared to the mature target detection algorithms YOLOv5s, YOLOv7, and SSD, it improves 4.8%, 4.5%, and 5.9% on mAP0.5:0.95, respectively. The results show that the improved model could accurately detect cotton weeds in complex field environments in real time and provide technical support for intelligent weeding research.

Chrysosplenium, a perennial herb in the family Saxifragaceae, prefers to grow in low light and moist environments and is divided into two sections of Alternifolia and Oppositifolia based on phyllotaxy. Although there has been some progress in the phylogeny of Chrysosplenium over the years, the phylogenetic position of some species is still controversial. In this study, we assembled chloroplast genomes (cp genomes) of 34 Chrysosplenium species and performed comparative genomic and phylogenetic analyses in combination with other cp genomes of previously known Chrysosplenium species, for a total of 44 Chrysosplenium species. The comparative analyses revealed that cp genomes of Chrysosplenium species were more conserved in terms of genome structure, gene content and arrangement, SSRs, and codon preference, but differ in genome size and SC/IR boundaries. Phylogenetic analysis showed that cp genomes effectively improved the phylogenetic support and resolution of Chrysosplenium species and strongly supported Chrysosplenium species as a monophyletic taxon and divided into three branches. The results also showed that the sections of Alternifolia and Oppositifolia were not monophyletic with each other, and that C. microspermum was not clustered with other Chrysosplenium species with alternate leaves, but with C. sedakowii into separate branches. In addition, we identified 10 mutational hotspot regions that could serve as potential DNA barcodes for Chrysosplenium species identification. In contrast to Peltoboykinia, the clpP and ycf2 genes of Chrysosplenium were subjected to positive selection and had multiple significant positive selection sites. We further detected a significant positive selection site on the petG gene between the two sections of Chrysosplenium. These evolutionary characteristics may be related to the growth environment of Chrysosplenium species. This study enriches the cp genomes of Chrysosplenium species and provides a reference for future studies on its evolution and origin.

Cotton is a crucial crop in the global textile industry, with major production regions including China, India, and the United States. While smart agricultural mechanization technologies, such as automated irrigation and precision pesticide systems, have improved crop management, weeds remain a significant challenge. These weeds not only compete with cotton for nutrients but can also serve as hosts for diseases, affecting both cotton yield and quality. Existing weed detection models perform poorly in the complex environment of cotton fields, where the visual features of weeds and crops are similar and often overlap, resulting in low detection accuracy. Furthermore, real-time deployment on edge devices is difficult. To address these issues, this study proposes an improved lightweight weed detection model, YOLO-WL, based on the YOLOv8 architecture. The model leverages EfficientNet to reconstruct the backbone, reducing model complexity and enhancing detection speed. To compensate for any performance loss due to backbone simplification, CA (cross-attention) is introduced into the backbone, improving feature sensitivity. Finally, AFPN (Adaptive Feature Pyramid Network) and EMA (efficient multi-scale attention) mechanisms are integrated into the neck to further strengthen feature extraction and improve weed detection accuracy. At the same time, the model maintains a lightweight design suitable for deployment on edge devices. Experiments on the CottonWeedDet12 dataset show that the YOLO-WL model achieved an mAP of 92.30%, reduced the detection time per image by 75% to 1.9 ms, and decreased the number of parameters by 30.3%. After TensorRT optimization, the video inference time was reduced from 23.134 ms to 2.443 ms per frame, enabling real-time detection in practical agricultural environments.

The genus Carthamus Linnaeus, which belongs to the tribe Cardueae in the Asteraceae family, originated in the Mediterranean region and consists of approximately 20 species worldwide. Understanding the phylogeny of the Carthamus is crucial for the cultivation of C. tinctorius. Although chloroplast genomes are widely used for species identification and evolutionary studies, there have been limited investigations on the chloroplast genomes of Carthamus species. In this study, we assembled the chloroplast genomes of C. persicus, C. tinctorius × C. persicus, and C. lanatus and combined them with the five chloroplast genomes of C. tinctorius for comparative genomic analysis. The sizes of the chloroplast genomes of C. lanatus, C. persicus, and C. tinctorius × C. persicus were 152,602 bp, 153,177 bp, and 153,177 bp, respectively. Comparative analysis showed that the chloroplast genome structures of the four Carthamus species were highly conserved. Additionally, the phylogenomic analysis demonstrated that the plastid genome and angiosperms353 dataset significantly improved the phylogenetic support of Carthamus species. This analysis supported Carthamus as a monophyletic taxon and its internal division into the sect. Carthamus and sect. Atractylis. The Carthamus was closely related to Carduncellus, Femeniasia, Phonus, and Centaurea. In conclusion, this study not only expands our understanding of the cp genomes of Carthamus species but also provides support for more comprehensive phylogenetic studies of Carthamus.