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
Administration route and trial repetition shape the effects of a commercial synbiotic on broiler production performance, cecal microbiota and pathogen colonization. [PDF]
Avberšek J +8 more
europepmc +1 more source
Weaving Regenerative Value Through Nature-Inclusive Stakeholder Engagement: Insights From Indigenous Businesses in the Sierra Nevada. [PDF]
Oberholzer S.
europepmc +1 more source
Trajectories of psychological distress and spinal pain in manual therapists during the COVID-19 pandemic in Sweden. [PDF]
Weiss N +3 more
europepmc +1 more source
Design and validation of a flexible framework for developing generic digital twins for Industry 4.0. [PDF]
Pribiš R +6 more
europepmc +1 more source
Blended learning and student engagement: a case of undergraduate business students in Nepal. [PDF]
Nepali S.
europepmc +1 more source
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