Intelligent semantic information processing and adaptive knowledge generation have become key enabling technologies for next-generation communication and information systems. This study proposes a New Quality of Translation Productivity framework based on Large Language Models (NQTP-LLM) for intelligent multilingual information processing and adaptive educational support. The framework integrates transformer-based neural machine translation, Direct Preference Optimization (DPO), Retrieval-Augmented Generation (RAG), semantic embedding representation, and human-in-the-loop optimization to enhance contextual consistency, semantic fidelity, and translation efficiency. A multimodal translation evaluation architecture is established using semantic feature extraction, contextual knowledge retrieval, quality assessment, and adaptive feedback mechanisms. Experiments conducted on the AI vs TTM Translation Evaluation Dataset demonstrate that the proposed framework achieves a BLEU score of 0.961, with substantial improvements in METEOR, ROUGE, chrF, BERTScore, COMET, BLEURT, Google-BLEU, and NIST metrics while reducing post-editing effort by 3.9%. The results verify the effectiveness of integrating intelligent knowledge retrieval, semantic information fusion, and adaptive optimization for high-accuracy multilingual information processing. The proposed framework provides a practical approach for intelligent communication systems, semantic information services, human–AI collaborative decision support, and next-generation knowledge-centric digital environments.
W. Zhou, X. Zhou· Advanced Electromagnetics· 0 citations
Accurate assessment of accident severity and regulatory deficiencies is essential for improving safety management and intelligent decision-making in complex industrial systems. This study proposes an interpretable machine learning framework to evaluate the influence of regulatory variables on coal mine accident severity by integrating data-driven prediction with SHAP-based feature attribution. Based on grounded-theory analysis of 382 accident investigation reports, more than 800 textual statements were condensed into 62 initial concepts, 13 secondary indicators, and five categories of regulatory factors, including design schemes, management systems, technical documents, organizational measures, and process methods. These variables were encoded and incorporated into Logistic Regression, C4.5, CART, CHAID, and Random Forest models for comparative analysis. Experimental results demonstrate that the Random Forest model achieves the best predictive performance in terms of accuracy and AUC, while SHAP analysis provides quantitative interpretation of the contribution and interaction of regulatory variables. The findings indicate that organizational measures, technical documentation, process methods, and management systems are the dominant determinants of accident severity, enabling transparent risk assessment and targeted intervention strategies. The proposed framework establishes an effective methodology for interpretable predictive analytics, intelligent safety monitoring, and data-driven decision support in complex engineering environments, offering valuable references for distributed sensing systems, industrial information fusion, and intelligent monitoring architectures related to Electromagnetic Waves, Antennas and Propagation engineering applications.
M. Li, X. Zhou, H. Li· Advanced Electromagnetics· 0 citations
