Experimental Investigation of Wave Propagation in Bio-Modified Nonlocal Couple-Stress Thermoelastic Media with Viscosity, Phase-Lag Thermal Diffusion, and Microstructural Effects

Reliable viral disease detection in clinical practice demands analytical frameworks that can effectively handle heterogeneous data, strict privacy requirements, and demographic variability. This paper presents a privacy-preserving and interpretable generative multi-modal learning framework aimed at achieving robust viral disease detection while incorporating bias mitigation, data harmonization, and real-time generalization. The proposed approach is informed by experimental observations from wave propagation in bio-modified nonlocal couple-stress thermoelastic media, where viscosity, phase-lag thermal diffusion, and microstructural effects were found to enhance signal stability, reduce attenuation, and promote uniform propagation. These experimentally established characteristics motivate the modeling of biomedical data in a manner that preserves structural consistency across multiple modalities, including medical imaging, physiological signals, and biochemical measurements. Privacy protection is ensured through distributed learning strategies and noise-aware representation modeling, allowing collaborative analysis without direct data sharing. Interpretability is embedded through physics-inspired constraints and attention-based feature analysis, supporting transparent and clinically meaningful decision processes. Bias is addressed using adaptive harmonization mechanisms guided by experimentally observed microstructural regularization effects. In addition, the incorporation of nonlocal interactions and phase-lag dependencies enables stable performance under noisy, time-varying clinical conditions. Validation results indicate notable improvements in detection accuracy, fairness, and robustness when compared with conventional multi-modal approaches. Overall, the framework establishes a meaningful connection between experimental wave mechanics and data-driven healthcare analytics, contributing toward dependable and trustworthy viral disease diagnostics.