Governing Dust:
State Responsibility, Labor Struggles, and the Politics of Pneumoconiosis in China, Australia, and the United States
Keywords：Pneumoconiosis; Black Lung Disease; Occupational Health Policy; Comparative Analysis; China; Australia; United States; Labor and Society; Governance
Abstract:
Pneumoconiosis remains one of the most persistent occupational diseases worldwide. This paper compares policy responses in China, Australia, and the United States, focusing on how states regulate dust exposure, provide worker protection, and frame responsibility. Australia highlights preventive innovation through real-time monitoring and material bans; China integrates legal enforcement with social welfare and poverty alleviation; the U.S. emphasizes compensation but struggles with enforcement and industry resistance. The comparison shows how different governance models reflect broader political and social structures, offering insight into the intersection of occupational health, regulation, and labor relations.
Analysis of Pneumoconiosis Patients’ Perception and Satisfaction with the Aid from “Da Ai Qing Chen”: A Cross-Sectional Study
Keywords: Pneumoconiosis, Social Support, Positive Coping Strategies, Patient Satisfaction, NGO Assistance
Abstract:
This study examines pneumoconiosis patients’ satisfaction with NGO Love Save Pneumoconiosis through social support and coping frameworks. Results indicate strong satisfaction, significantly linked to social support (r=0.508, p<0.01) and positive coping (r=0.503, p<0.01). Regression confirms both factors enhance satisfaction. Key challenges remain, including unstable financial aid, inadequate health support, and burdens from advanced illness. Recommendations highlight strengthening financial assistance, improving healthcare access, and promoting regional equity. This study underscores the vital role of NGO-driven support in pneumoconiosis care.
A Dual-Path Approach to Fake News Detection Based on Ensemble Learning and Fine-Tuned Large Language
Keywords：fake news detection, machine learning, large language model, ensembled learning, fine-tuned
Abstract:
The rapid proliferation of misinformation in today’s digital ecosystem poses threats to public’s trust on institutions and informed decision-making. To mitigate these challenges, the development of efficient and automated fake news detection systems has become increasingly essential. This study presents a dual-path framework, FIND, an ensembled model, combines Random Forest classifiers, fine-tuned BERT models, and prompt-engineered LLMs to address mainstream detection tasks with high accuracy. Additionally, we fine-tune LLMs to adapt to long-tail news (ie news with uncommon topics) distributions, enhancing model generalizability and scalability. Experimental evaluations on benchmark datasets confirm the robustness and effectiveness of the proposed method in handling complex fake news, proving its applicability in real-world scenarios.