This paper proposes a Diffusion Model-Optimized Neural Radiance Field (DT-NeRF) method, aimed at enhancing detail recovery and multi-view consistency in 3D scene reconstruction. By combining diffusion models with Transformers, DT-NeRF effectively restores details under sparse viewpoints and maintains high accuracy in complex geometric scenes. Experimental results demonstrate that DT-NeRF significantly outperforms traditional NeRF and other state-of-the-art methods on the Matterport3D and ShapeNet datasets, particularly in metrics such as PSNR, SSIM, Chamfer Distance, and Fidelity. Ablation experiments further confirm the critical role of the diffusion and Transformer modules in the model's p... More >

Chronic Pelvic Inflammatory Disease (CPID) poses significant challenges to women's health, necessitating advanced management strategies. This paper provides a comprehensive review of artificial intelligence (AI)-based health management techniques for PID, focusing on their potential to enhance diagnosis, treatment personalization, and long-term monitoring. By synthesizing Bayesian probabilistic frameworks with ensemble Machine Learning architectures, we systematically evaluate AI-driven solutions for PID pathophysiology analysis, therapeutic efficacy prediction, and patient-specific intervention planning. These approaches collectively enhance diagnostic precision while addressing key challen... More >

Poetry, as a profound and creative form of human expression, presents unique challenges in interpretation and summarization due to its reliance on figurative language, symbolism, and deeper meanings. Building upon the PoemSum dataset, which introduced the task of poem summarization, we extend its scope by exploring multimodal applications. Specifically, we implement and fine-tune two state-of-the-art abstractive summarization models—BART and T5—to generate concise and meaningful interpretations of poems, focusing on figurative summarization that captures metaphorical and symbolic elements inherent in poetic language. These summaries are then transformed into visual representations using... More >

To address the issues of low solving efficiency and susceptibility to local optima in multi-unmanned aerial vehicle (multi-UAV) task allocation algorithms within urban areas, this study constructs a task allocation model aiming to minimize economic costs for material delivery and reduce the urgency of rescue task demands. A multi-strategy clustering ant colony optimization algorithm (KMACO) is proposed for solution. Specifically, the K-means clustering method is utilized to partition the number of rescue tasks assigned to each UAV. In the ant colony optimization algorithm, a pheromone update strategy and a random evolution strategy are introduced to guide population search directions, thereb... More >

This paper explores factors influencing consumer satisfaction in automotive spare parts e-commerce through text mining and sentiment analysis of Taobao reviews. By applying TF-IDF (Term Frequency-Inverse Document Frequency), semantic network analysis, and LDA (Latent Dirichlet Allocation) topic modeling, four core themes are identified: Logistics, Quality, Price, and Customer Service. A domain-specific sentiment lexicon constructed via the SO-PMI method reveals that positive reviews predominantly emphasize product reliability and logistics efficiency, while negative feedback focuses on installation complexity and inconsistent specifications. Based on these findings, targeted recommendations... More >