FKF Transform Based Multi-Scale Analysis and Digital Twin–Enabled Supply-Chain Resilience
TL;DR
Future work must prioritize real-time fusion of connected-vehicle and smart-infrastructure data streams, construction of scalable multi-resolution Digital Twin environments that span both supply chains and urban mobility networks, development of trustworthy AI models for predictive and prescriptive control, and computationally efficient multi-scale spectral analysis of complex temporal dynamics.
Abstract
The convergence of AI, Blockchain, IoT, Digital Twins, quantum computing, and FKF spectral methods constitutes a decisive research direction for next-generation logistics, especially when examined through the lens of Smart Mobility and Intelligent Transportation Systems [9]. Future work must prioritize real-time fusion of connected-vehicle and smart-infrastructure data streams, construction of scalable multi-resolution Digital Twin environments that span both supply chains and urban mobility networks, development of trustworthy AI models for predictive and prescriptive control, realization of practical hybrid quantum–classical optimizers for the combinatorial problems that dominate intelligent transportation and logistics, and computationally efficient multi-scale spectral analysis of complex temporal dynamics. Empirical validation across transportation, inventory, energy, and disruption scenarios incorporating advances in battery technologies [5], [10], solar-assisted and hybrid vehicle architectures [12], [28], and Industry 5.0 human-centric automation [27]will be indispensable. Realizing these advances will transform intelligent logistics from a conceptual integration into operational, sustainable, and resilient supply-chain systems that fully exploit the emerging capabilities of smart mobility ecosystems [1]–[28].