To address the trade-off between accuracy and cross-city generalization in traffic flow estimation, a research team from The ...

In AIoT-based systems, sensors continuously collect high-frequency data such as vibration, temperature, pressure, and electrical signals. These data streams are processed by machine learning and deep ...

Urban systems rely on continuous data streams from heterogeneous sensors embedded in roads, vehicles, buildings, medical ...

Network-wide traffic flow, which represents the dynamic traffic volumes on each link of a road network, is fundamental to smart cities. However, the ...

Researchers at Tsinghua University developed PriorFusion, a unified framework that integrates semantic, geometric, and ...

Troy Mahr, Director, Rockwell Automation, explains how achieving autonomous operations requires integrating industrial data and AI to eliminate silos, enable predictive and adaptive capabilities, and ...

Reverse Logistics, Artificial Intelligence, Circular Economy, Supply Chain Management, Sustainability, Machine Learning Share and Cite: Waditwar, P. (2026) De-Risking Returns: How AI Can Reinvent Big ...

If you could only use one word to describe the global materials handling sector in 2025, it would surely have to be ...

Imaging technology has transformed how we observe the universe—from mapping distant galaxies with radio telescope arrays to ...

At the core of every AI coding agent is a technology called a large language model (LLM), which is a type of neural network ...

Cooperative autonomous driving represents the frontier of intelligent transportation systems, where vehicles, infrastructure, and other road users share ...

Step aside, LLMs. The next big step for AI is learning, reconstructing and simulating the dynamics of the real world.