In this article, we propose a resource allocation scheme for air slicing in unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (V2X) communications. We consider a scenario, where multiple flexible UAVs are deployed as the aerial base station (BS) to assist terrestrial BS for providing service to vehicular users with the objective of maximizing the bandwidth efficiency while concurrently guaranteeing the transmission rate and the latency by adopting network slicing. Due to the uncertainty of the stochastic environment in the scenario, we formulate the optimization problem to be a stochastic game, which is an extension of game theory to Markov decision process-like environments for the case of multiple adaptive agents are involved to compete goals simultaneously. Nevertheless, the dynamic nature of both UAVs and vehicles pose the difficulty of perceiving and interacting with the unknown environment, the long short-term memory algorithm is used for extracting the features of the observation and making forecast on the mobility of UAVs and vehicles. Simulation results adduce the validity of the proposed scheme as compared with two benchmark schemes: Deep Q-network and deep deterministic policy gradient.

Leaf diseases such as Mosaic disease and Black Rot are among the most common diseases affecting apple leaves, significantly reducing apple yield and quality. Detecting leaf diseases is crucial for the prevention and control of these conditions. In this paper, we propose incorporating rotated bounding boxes into deep learning-based detection, introducing the ProbIoU loss function to better quantify the difference between model predictions and real results in practice. Specifically, we integrated the Plant Village dataset with an on-site dataset of apple leaves from an orchard in Weifang City, Shandong Province, China. Additionally, data augmentation techniques were employed to expand the dataset and address the class imbalance issue. We utilized the EfficientNetV2 architecture with inverted residual structures (FusedMBConv and S-MBConv modules) in the backbone network to build sparse features using a top–down approach, minimizing information loss. The inclusion of the SimAM attention mechanism effectively captures both channel and spatial attention, expanding the receptive field and enhancing feature extraction. Furthermore, we introduced depth-wise separable convolution and the CAFM in the neck network to improve feature fusion capabilities. Finally, experimental results demonstrate that our model outperforms other detection models, achieving 93.3% mAP@0.5, 88.7% Precision, and 89.6% Recall. This approach provides a highly effective solution for the early detection of apple leaf diseases, with the potential to significantly improve disease management in apple orchards.

The semantic segmentation of remote sensing images is extensively used in crop cover and type analysis and environmental monitoring. In the semantic segmentation of remote sensing images, owning to the specificity of remote sensing images, not only the local context is required, but also the global context information makes an important role in it. Inspired by the powerful global modeling capability of Swin Transformer, we propose the Local and Spatial Enhancement Net (LSENet) network, which follows the encoder–decoder architecture of the UNet network. In the encoding phase, we propose spatial enhancement module (SEM), which helps Swin Transformer further enhance feature extraction by encoding spatial information. In the decoding stage, we propose local enhancement module (LEM), which is embedded in the Swin Transformer to improve the Swin Transformer to assist the network to obtain more local semantic information so as to classify pixels more accurately, especially in the edge region, the adding of LEM enables to obtain smoother edges. The experimental results on the Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method. Specifically, the mIoU metric is 78.58% on the Potsdam dataset and 72.59% on the Vaihingen dataset.

Geopolymer is a sustainable low-carbon cementitious material that is able to incorporate large amounts of solid waste as precursors or activators. As the proportion of municipal solid waste incineration continues to rise in China, the large-scale generation of municipal solid waste incineration fly ash (MSWI FA) has emerged as a significant challenge. The production of geopolymers represents a potential pathway for the comprehensive utilization of MSWI FA. However, most studies have reported that geopolymers containing MSWI FA exhibit low strength, which diminishes their economic value. Furthermore, the unclear environmental risks associated with MSWI FA-based geopolymers have impeded their broader application. This study explores the use of MSWI FA as a substitute for ground granulated blast furnace slag (GGBS) or coal fly ash (CFA) in the production of high-performance geopolymers, achieving compressive strengths exceeding 60 MPa, even when the MSWI FA content reaches 50%. A synergistic effect is observed between MSWI FA and CFA, which enhances the reactivity of CFA. With reasonable formulation, the environmental risks of geopolymers containing MSWI FA are manageable in normal rainfall scenarios. However, there remains a potential risk of soil and groundwater contamination under extreme conditions, such as acid rain.