Compared to text-only online fake news, combined graphic forms of fake news are more likely to gain people's trust. Most of these forms of fake news have been detected using multimodal feature fusion, with less attention being paid to the correlation between two modalities and the interaction within and between individual modalities. To address this problem, we propose a multimodal mechanism based on CLIP for fake news detection, referred to as CLIP-FND. First, the visual encoder and text encoder of the large-scale graphical pre-training model CLIP are used to unify and map the image and text data into the same feature space respectively. Then, the attention mechanism and the bilinear pooling method are used to fuse the text feature vector and the visual feature vector and input to the fake news detector for fake news detection. The experimental results show that the accuracy and F1 value of the model are much higher than the traditional multimodal fake news detection model on the publicly available dataset, which improves the fake news detection effect.

Adapting Large Language Models (LLMs) to specialized domains like geriatric care remains a significant challenge due to the limited availability of domain-specific data and the difficulty of achieving efficient yet effective fine-tuning. Current methods often fail to effectively harness domain-specific causal insights, which are crucial for understanding and solving complex problems in low-resource domains.To address these challenges, we propose Causality-Aware, Parameter-Efficient Learning (CPEL), a novel framework that leverages domain-specific causal relationships to guide a multi-layer, parameter-efficient fine-tuning process for more effective domain adaptation. By embedding causal reasoning into the model’s adaptation pipeline, CPEL enables efficient specialization in the target domain while maintaining strong task-specific performance. Specifically, the Causal Prompt Generator of CPEL extracts and applies domain-specific causal structures, generating adaptive prompts that effectively guide the model’s learning process. Complementing this, the MPEFT module employs a dual-adapter mechanism to balance domain-level adaptation with downstream task optimization. This cohesive design ensures that CPEL achieves resource efficiency while capturing domain knowledge in a structured and interpretable manner. Based on this framework, we delved into its application in the field of geriatric care and trained a specialized large language model (Geriatric Care LLaMA) tailored for the aged-care domain, leveraging its capacity to efficiently integrate domain expertise. Experimental results from question-answering tasks demonstrate that CPEL improves ROUGE scores by 9–14% compared to mainstream LLMs and outperforms frontier models by 1–2 points in auto-scoring tasks. In summary, CPEL demonstrates robust generalization and cross-domain adaptability, highlighting its scalability and effectiveness as a transformative solution for domain adaptation in specialized, resource-constrained fields.

Industrial defect detection requires accurate localization of small and irregular defects under complex backgrounds while maintaining low computational cost for deployment-oriented applications. To address the redundancy and limited small-defect representation of RT-DETR in industrial scenarios, this paper proposes MC-DETR, a lightweight detector built on RT-DETR. The method introduces an MSInit-cross stage partial network backbone for multi-scale lightweight feature extraction, a curvature-guided token attention module for defect-relevant token interaction, and an NWDIoU-based regression strategy for more stable localization. Experiments on a self-built industrial defect dataset and the public NEU-DET dataset show that MC-DETR reduces parameters by 34.5% and GFLOPs by 21.1% compared with RT-DETR-R18, while achieving mAP0.5 of 0.803 and mAP0.5:0.95 of 0.458 on the self-built dataset. The results indicate that MC-DETR provides a favorable accuracy-complexity trade-off for industrial defect inspection.