To address the challenges associated with wind power integration, this paper analyzes the impact of distributed renewable energy on the voltage of the distribution network. Taking into account the fast control of photovoltaic inverters and the unique characteristics of photovoltaic arrays, we establish an active distribution network voltage reactive power-optimization model for planning the active distribution network. The model involves solving the original non-convex and non-linear power-flow-optimization problem. By introducing the second-order cone relaxation algorithm, we transform the model into a second-order cone programming model, making it easier to solve and yielding good results. The optimized parameters are then applied to the IEEE 33-node distribution system, where the phase angle of the node voltage is adjusted to optimize the reactive power of the entire power system, thereby demonstrating the effectiveness of utilizing a second-order cone programming algorithm for reactive power optimization in a comprehensive manner. Subsequently, active distribution network power quality control is implemented, resulting in a reduction in network loss from 0.41 MW to 0.02 MW. This reduces power loss rates, increases utilization efficiency by approximately 94%, optimizes power quality management, and ensures that users receive high-quality electrical energy.

Accurate identification of cotton leaf pests and diseases is essential for sustainable cultivation but is challenged by complex backgrounds, diverse pest morphologies, and varied symptoms, where existing deep learning models often show insufficient robustness. To address these challenges, RDL-YOLO model is proposed in this study. In the proposed model, RepViT-Atrous Convolution (RepViT-A) is employed as the backbone network to enhance local–global interaction and improve the response intensity and extraction accuracy of key lesion features. In addition, the Dilated Dense Convolution (DDC) module is designed to achieve a dynamic multi-scale receptive field, enabling the network to adapt to lesion defects of different shapes and sizes. LDConv further optimizes the effect of feature fusion. Experimental results showed that the mean Average Precision (mAP) of the proposed model reached 77.1%, representing a 3.7% improvement over the baseline YOLOv11. Compared with leading detectors such as Real-Time Detection Transformer (RT-DETR), You Only Look Once version 11 (YOLOv11), DETRs as Fine-grained Distribution Refinement (D-FINE), and Spatial Transformer Network-YOLO (STN-YOLO). RDL-YOLO exhibits superior performance, enhanced reliability, and strong generalization capabilities in tests on the cotton leaf dataset and public datasets. This advancement offers a practical technical solution for improved agricultural pest and disease management.

With the continuous increase in global energy demand and growing environmental awareness, the utilization of renewable energy has become a worldwide consensus. In order to address the challenges posed by the intermittent and unpredictable nature of renewable energy in distributed power distribution networks, as well as to improve the economic and operational stability of distribution systems, this paper proposes the establishment of an active distribution network capable of accommodating renewable energy. The objective is to enhance the efficiency of new energy utilization. This study investigates optimal scheduling models for energy storage technologies and economic-operation dispatching techniques in distributed power distribution networks. Additionally, it develops a comprehensive demand response model, with real-time pricing and incentive policies aiming to minimize load peak–valley differentials. The control mechanism incorporates time-of-use pricing and integrates a chaos particle swarm algorithm for a holistic approach to solution finding. By coordinating and optimizing the control of distributed power sources, energy storage systems, and flexible loads, the active distribution network achieves minimal operational costs while meeting demand-side power requirements, striving to smooth out load curves as much as possible. Case studies demonstrate significant enhancements during off-peak periods, with an approximately 60% increase in the load power overall elevation of load factors during regular periods, as well as a reduction in grid loads during evening peak hours, with a maximum decrease of nearly 65 kW. This approach mitigates grid operational pressures and user expense, effectively enhancing the stability and economic efficiency in distribution network operations.

Wheat disease detection is a crucial component of intelligent agricultural systems in modern agriculture. However, at present, its detection accuracy still has certain limitations. The existing models hardly capture the irregular and fine-grained texture features of the lesions, and the results of spatial information reconstruction caused by standard upsampling operations are inaccuracy. In this work, the GDFC-YOLO method is proposed to address these limitations and enhance the accuracy of detection. This method is based on YOLOv11 and encompasses three key aspects of improvement: (1) a newly designed Ghost Dynamic Feature Core (GDFC) in the backbone, which improves the efficiency of disease feature extraction and enhances the model’s ability to capture informative representations; (2) a redesigned neck structure, Disease-Focused Neck (DF-Neck), which further strengthens feature expressiveness, to improve multi-scale fusion and refine feature processing pipelines; and (3) the integration of the Powerful Intersection over Union v2 (PIoUv2) loss function to optimize the regression accuracy and convergence speed. The results showed that GDFC-YOLO improved the average accuracy from 0.86 to 0.90 when the cross-overmerge threshold was 0.5 (mAP@0.5), its accuracy reached 0.899, its recall rate reached 0.821, and it still maintained a structure with only 9.27 M parameters. From these results, it can be known that GDFC-YOLO has a good detection performance and stronger practicability relatively. It is a solution that can accurately and efficiently detect crop diseases in real agricultural scenarios.

In recent years, a huge amount of text information requires processing to support the diagnosis and treatment of diabetes in the medical field; therefore, the named entity recognition of diabetes (DNER) is giving rise to the popularity of this research topic within this particular field. Although the mainstream methods for Chinese medical named entity recognition can effectively capture global context information, they ignore the potential local information in sentences, and hence cannot extract the local context features through an efficient framework. To overcome these challenges, this paper constructs a diabetes corpus and proposes the RMBC (RoBERTa Multi-scale CNN BiGRU Self-attention CRF) model. This model is a named entity recognition model that unites multi-scale local feature awareness and the self-attention mechanism. This paper first utilizes RoBERTa-wwm to encode the characters; then, it designs a local context-wise module, which captures the context information containing locally important features by fusing multi-window attention with residual convolution at the multi-scale and adds a self-attention mechanism to address the restriction of the bidirectional gated recurrent unit (BiGRU) capturing long-distance dependencies and to obtain global semantic information. Finally, conditional random fields (CRF) are relied on to learn of the dependency between adjacent tags and to obtain the optimal tag sequence. The experimental results on our constructed private dataset, termed DNER, along with two benchmark datasets, demonstrate the effectiveness of the model in this paper.

Knowledge graph embedding (KGE) has been identified as an effective method for link prediction, which involves predicting missing relations or entities based on existing entities or relations. KGE is an important method for implementing knowledge representation and, as such, has been widely used in driving intelligent applications w.r.t. question-answering systems, recommendation systems, and relationship extraction. Models based on convolutional neural networks (CNNs) have achieved good results in link prediction. However, as the coverage areas of knowledge graphs expand, the increasing volume of information significantly limits the performance of these models. This article introduces a triple-attention-based multi-channel CNN model, named ConvAMC, for the KGE task. In the embedding representation module, entities and relations are embedded into a complex space and the embeddings are performed in an alternating pattern. This approach helps in capturing richer semantic information and enhances the expressive power of the model. In the encoding module, a multi-channel approach is employed to extract more comprehensive interaction features. A triple attention mechanism and max pooling layers are used to ensure that interactions between spatial dimensions and output tensors are captured during the subsequent tensor concatenation and reshaping process, which allows preserving local and detailed information. Finally, feature vectors are transformed into prediction targets for embedding through the Hadamard product of feature mapping and reshaping matrices. Extensive experiments were conducted to evaluate the performance of ConvAMC on three benchmark datasets compared with state-of-the-art (SOTA) models, demonstrating that the proposed model outperforms all compared models across all evaluation metrics on two of the datasets, and achieves advanced link prediction results on most evaluation metrics on the third dataset.