Recently, numerous inspirational approaches have been proposed to enhance the visual quality of the images captured under poor lighting conditions. Simultaneously, in order to accommodate diverse user esthetics, researchers have explored customized operations within the enhancement process. However, most existing studies ignore the significance of the chrominance component, which often leads to unsatisfactory results in terms of color. To address this issue, we novelly decompose the low-light image enhancement (LLIE) task into the brightening and colorization subtasks and develop a decoupled network called CCNet for colorization-inspired customized enhancement. Specifically, the brightening subtask aims to restore images with normal contrast, less noise, and sharper details. While the colorization subtask utilizes the chrominance information from low-light images as color guidance to predict rich chrominance in enhanced images. Then, in the inference stage, users can adjust the color style or the saturation of color guidance to obtain customized results. Extensive experiments demonstrate that our proposed method achieves superior performance in both general and customized LLIE tasks—particularly in terms of improving chrominance components. Code is available at: https://github.com/FVL2020/CCNet.

To enhance the accuracy and reliability of large language models (LLMs) in regulatory question-answering tasks, this study addresses the complexity and domain-specificity of regulatory texts by designing a retrieval-augmented generation (RAG) testing framework. It proposes a dimensionality reduction-based semantic similarity measurement method and a retrieval optimization approach leveraging information reasoning. Through the construction of the technical route of the intelligent knowledge management system, the semantic understanding capabilities of multiple mainstream embedding models in the text matching of financial regulations are systematically evaluated. The workflow encompasses data processing, knowledge base construction, embedding model selection, vectorization, recall parameter analysis, and retrieval performance benchmarking. Furthermore, the study innovatively introduces a multidimensional scaling (MDS) based semantic similarity measurement method and a question-reasoning processing technique. Compared to traditional cosine similarity (CS) metrics, these methods significantly improved recall accuracy. Experimental results demonstrate that, under the RAG testing framework, the mxbai-embed-large embedding model combined with MDS similarity calculation, Top-k recall, and information reasoning effectively addresses core challenges such as the structuring of regulatory texts and the generalization of domain-specific terminology. This approach provides a reusable technical solution for optimizing semantic matching in vertical-domain RAG systems, particularly for MDSs such as law and finance.

Low-Light Image Enhancement (LLIE) is crucial for enhancing the visual quality of images captured in dim lighting conditions, particularly considering the widespread mobile photography. Meanwhile, image compression is an essential component in mobile photography to balance image quality and storage space. However, most existing LLIE methods overlook the impact of compression, leading to significant performance degradation. Therefore, we draw the first attention to Compressed Low-Light Image Enhancement (CLLIE) and propose our solution. In contrast to many existing methods employing end-to-end architectures for direct learning of complex mappings, we introduce a two-decoder decoupling strategy aimed at achieving efficient decoupling and enhanced mapping learning. To mitigate performance degradation caused by the insufficient robustness of existing priors, we integrate two robust priors with our proposed Brightness-Guided Transformer (BGT) and Semantic-Guided Channel Attention Block (SGCAB), facilitating robust enhancement. Extensive experiments demonstrate a significant improvement of our proposed method over existing SOTAs, achieving up to 1.8 dB higher PSNR, which indicates the effectiveness of our design in suppressing the effect of compression and highlights the importance of considering compression in LLIE.

Large language models (LLMs) offer new opportunities for intelligent decision support in logistics network modeling and optimization, yet existing studies mainly treat them as algorithmic assistants, direct optimizers, or end-to-end generators. Less attention has been given to organizing open-ended human–LLM interaction into a reliable decision process for strict constraints, incomplete specifications, and outputs requiring independent verification. This study develops a knowledge-based human–LLM interaction decision support system (K-HLIDSS) for logistics network modeling and optimization, using UAV emergency scheduling as a representative application setting. K-HLIDSS integrates model-element input structuring, knowledge-guided reasoning, and feedback verification to support a traceable and verifiable optimization decision chain. Six experiments combine mechanism-level benchmark validation and application-level case validation, covering structured model prior knowledge, model-element verification, workflow-based algorithm generation and solution execution, multi-stage five-algorithm generation with path-level reverse verification, real-world UAV emergency scheduling, and dynamic new-demand re-dispatching. On Cordeau benchmark instances, the four benchmark experiments show that structured prior knowledge increases model-element accuracy from 79.17% to 99.58%, and model-element verification raises it to 100.00%. Feedback verification increases the valid route output rate from 22.50% to 90.00% and reduces constraint violations from 17.50% to 1.25%. Across 33 instances, verified results selected instance by instance from five generated algorithms yield an average gap of 2.20%. In Shenzhen, China, two case experiments further demonstrate the system's capability for network modeling, route generation, and dynamic re-dispatching under realistic spatial and demand conditions. K-HLIDSS offers a process-level pathway for reliable AI-assisted logistics network optimization. Code and data are available at https://github.com/outhouse1/AI_UAV