Assessing Recent Trends in Supply Chain Management, Technology Adoption and Performance in Storage Sector

Abstract

The electrophysiological changes that accompany Parkinson’s disease are expressed not only functionally related sensor groups, and a Chebyshev spectral convolution propagates information over this higher-order structure. A temporal convolutional network with dilated causal filters models the non-stationary evolution of connectivity, and a squeeze-and-excitation module recalibrates the five band streams before classification. On the public University of California San Diego resting-state dataset the network reaches 97.85 percent accuracy, a 97.80 percent F1-score and an area under the curve of 0.998 under a stratified protocol, and it preserves 90.36 percent accuracy under a subject-independent protocol. Ablation and interpretability analyses confirm that directed beta-band coupling over sensorimotor cortex drives the decision.