Named Entity Recognition (NER) plays a crucial role in the field of Natural Language Processing, holding significant value in applications such as information extraction, knowledge graphs, and question–answering systems. However, Chinese NER faces challenges such as semantic complexity, uncertain entity boundaries, and nested structures. To address these issues, this study proposes an innovative approach, namely Multi-Granularity BERT Adapter and Efficient Global Pointer (MGBERT-Pointer). The semantic encoding layer adopts Multi-Granularity Adapter (MGA), while the decoding layer employs Efficient Global Pointer (EGP) network, ensuring collaborative functionality. The MGA, incorporating Character Adapter, Entity Adapter, and Lexicon Adapter through interactive mechanisms, are deeply integrated into the BERT base, significantly enhancing the model’s ability to handle complex contexts and ambiguities. The EGP, utilizing Rotary Position Embedding, resolves the issue of insufficient boundary information in traditional attention mechanisms, thereby improving the model’s understanding and recognition of nested entity structures. Experimental results on four public datasets demonstrate a significant enhancement in Chinese NER performance achieved by the MGBERT-Pointer model.

The thyroid gland is an important and essential endocrine organ for the human body that regulates metabolism, growth and development by secreting thyroid hormones. Thyroid nodules are irregular masses caused by lesions that can reflect the main clinical manifestation of thyroid abnormalities. Delineating the boundaries of thyroid nodules from ultrasound images is an indispensable part of computer-aided diagnosis systems and medical imaging diagnosis for thyroid diseases. However, automatic segmentation of thyroid nodules is still a challenging task because of the similarity between the heterogeneous appearance and its background. In this study, we propose a novel framework for thyroid nodule segmentation that exploits multiscale anatomical features to build a late-stage fusion method based on adversarial training. The introduced architecture can handle blurred and uneven tissue regions during the thyroid nodule segmentation process. By adversarial training approaches, our segmentation block S adopts a framework with three different fusion scales, and discrimination block D employs a fully convolutional encoder-decoder architecture. Extensive experimental results demonstrate that AMSeg outperforms popular methods in terms of thyroid nodule segmentation. The proposed framework achieves Dice, Hd95 (95% Hausdorff distance), Jaccard, and precision values of 83.06%, 23.13, 74.18%, and 87.82%, respectively. As an end-to-end network, the AMSeg can effectively replace manual segmentation methods and has great prospects in clinical applications.

The split delivery vehicle routing problem (SDVRP) is a classic combinatorial optimization problem, which is usually solved using a heuristic algorithm. The ant colony optimization algorithm is an excellent heuristic algorithm that has been successfully applied to solve various practical problems, and it has achieved good results. However, in the existing ant colony optimization algorithms, there are issues with weak targeting of different customer selection strategies, difficulty in balancing convergence speed and global search ability, and a predisposition to become trapped in local optima. To solve these problems, this paper proposes an improved ant colony algorithm (IACA). First, in terms of customer point selection, the initial customer and noninitial customer selection strategies are proposed for different customers, and the adaptive selection threshold is designed. Second, in terms of pheromone processing, an initial pheromone distribution method based on a greedy strategy, a pheromone backtracking mechanism, and an adaptive pheromone volatile factor are proposed. Finally, based on the 2-opt local search method, vehicle path self-search and intervehicle path search are proposed to further improve the quality of the solution. This paper tests the performance of the IACA on datasets of different scales. The experimental results show that compared with the clustering algorithm, artificial bee colony algorithm, particle swarm optimization algorithm, traditional ant colony algorithm, and other algorithms, the IACA can achieve more competitive results. Specifically, compared to the path length calculated by other algorithms, the path length calculated by IACA decreased by an average of 1.58%, 4.28%, and 3.64% in small, medium, and large-scale tests, respectively.

Damaged image inpainting is one of the hottest research fields in computer image processing. The development of deep learning, especially Convolutional Neural Network (CNN), has significantly enhanced the effect of image inpainting. However, the direct connection between convolution layers may increase the risk of gradient disappearance or overfitting during training process. In addition, pixel artifacts or visual inconsistencies may occur if the damaged area is inpainted directly. To solve the above problems, we propose a novel Dense Gated Convolutional Network (DGCN) for generative image inpainting by modifying the gated convolutional network structure in this paper. Firstly, Holistically-nested edge detector (HED) is utilized to predict the edge information of the missing areas to assist the subsequent inpainting task to reduce the generation of artifacts. Then, dense connections are added to the generative network to reduce the network parameters while reducing the risk of instability in the training process. Finally, the experimental results on CelebA and Places2 datasets show that the proposed model achieves better inpainting results in terms of PSNR, SSIM and visual effects compared with other classical image inpainting models. DGCN has the common advantages of gated convolution and dense connection, which can reduce network parameters and improve the inpainting effect of the network.

Accurate medical image segmentation plays a critical role in clinical diagnosis, treatment planning, and a wide range of healthcare applications. Although U-shaped CNNs and Transformer-based architectures have shown promise, CNNs struggle to capture long-range dependencies, whereas Transformers suffer from quadratic growth in computational cost as image resolution increases. To address these issues, we propose HCMUNet, a novel medical image segmentation model that innovatively combines the local feature extraction capabilities of CNNs with the efficient long-range dependency modeling of Mamba, enhancing feature representation while reducing computational cost. In addition, HCMUNet features a redesigned skip connection and a novel attention module that integrates multi-scale features to recover spatial details lost during down-sampling and to promote richer cross-dimensional interactions. HCMUNet achieves Dice Similarity Coefficients (DSC) of 90.32%, 81.52%, and 92.11% on the ISIC 2018, Synapse multi-organ, and ACDC datasets, respectively, outperforming baseline methods by 0.65%, 1.05%, and 1.39%. Furthermore, HCMUNet consistently outperforms U-Net and Swin-UNet, achieving average Dice score improvements of approximately 5% and 2% across the evaluated datasets. These results collectively affirm the effectiveness and reliability of the proposed model across different segmentation tasks.