Results 201 to 210 of about 1,671,103 (298)
AI in chemical engineering: From promise to practice
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
China's Pharmaceutical Ascent: Opportunity for Global Health, Test for US Leadership. [PDF]
Babul A, Mahdavi P, Hussain M, Babul N.
europepmc +1 more source
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Growth in the Size of Peritoneal Dialysis Facilities: A Path Toward Higher Quality or a Missed Opportunity? [PDF]
Kassem H, Erickson KF, Erickson KF.
europepmc +1 more source
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
Legalization dilemma and solution of China's professional sports league governance. [PDF]
Wang Q, Zhang Z, Han G.
europepmc +1 more source
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
Context Awareness and Human–Robot Interaction Optimization for Museum Intelligent Guide Robot
This study presents a context‐aware human–robot interaction framework designed for intelligent museum guide robots. The system features a three‐layer architecture—perception, understanding, and behavior execution—that enables adaptive and meaningful interactions with museum visitors.
Anna Zou, Yue Meng, Shijing Tong
wiley +1 more source
Competitive swarm reinforcement learning improves stability and performance of deep reinforcement learning. [PDF]
Huang X +5 more
europepmc +1 more source

