Vol. 1 No. 7 (2025): AI-Driven Innovations in Cross-Domain Applications: From Logistics and Healthcare to Agriculture and Education

This issue presents cutting-edge research exploring artificial intelligence's transformative potential across diverse domains. The collection features five groundbreaking studies that demonstrate AI's capacity to address complex challenges through innovative methodological frameworks and practical implementations. The issue opens with Wang's Bayesian Network analysisexamining how trade tensions impact global aviation logistics, providing a probabilistic risk assessment framework for supply chain resilience. The healthcare sector is represented by Li and Zhao's thoracic fracture detection system, which integrates YOLOv12 with BiFPN architecture to achieve remarkable diagnostic accuracy improvements. In agricultural technology, Li and Liu's GoatFaceNetdemonstrates lightweight facial recognition for livestock management, achieving 94.2% accuracy in open-set recognition scenarios despite high inter-animal similarity. The educational dimension is addressed by Liu et al.'s OBE-AI model, which bridges secondary-university physics education gaps through intelligent competency cultivation frameworks. Complementing these applications, Yang et al.'s AttentionHR frameworkleverages Transformer architecture for employee turnover prediction in IT industries, achieving 88.16% accuracy while identifying critical retention factors like salary satisfaction and technological pressure. Collectively, these studies highlight AI's evolving role in solving domain-specific problems through advanced neural architectures, probabilistic modeling, and adaptive learning mechanisms. The issue contributes significantly to both methodological innovation and practical implementation across logistics, healthcare, agriculture, education, and human resource management.