Recent research:
The collected publications reveal a cohesive program focused on applying Industry 4.0, IoT, and AI to improve manufacturing, energy, education, and healthcare. The core theme centers on integrating smart technologies (edge/fog computing, RAMI 4.0 alignment, MES, and predictive maintenance) with robust data-driven approaches to enhance reliability, efficiency, and safety in industrial and educational settings. Methodologies span from practical system integrations (MES deployments on modular production systems, fog/edge architectures, and RAMI 4.0 mappings) to advanced analytics and AI-driven monitoring (fault diagnosis in induction motors via Park transform, neural networks for fault detection, infrared thermography, and computer vision for part recognition). Several studies emphasize real-time or near-real-time decision support, latency reduction, and scalable architectures, often validated through experimental benches, lab deployments, or pilot implementations. Overall impact highlights improved load balancing, reduced latency, enhanced predictive maintenance, safer autonomous robotics, immersive learning tools, and non-invasive health monitoring, demonstrating a trajectory from theory to deployable IIoT solutions and intelligent systems in varied sectors.