Global research agenda for medical education regulation: findings from a nominal group consensus exercise. [PDF]
Bollela VR +17 more
europepmc +1 more source
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
Bridging the Gap Between Research Agendas and Practice: Insights from a Novel Evaluation Method
Powell AR +4 more
europepmc +1 more source
The development of BE-EMPOWERed: Belgian program Enhancing the uptake and Effectiveness of a Multifactorial falls Prevention intervention in Older community-dWElling peRsons. [PDF]
Vandervelde S +8 more
europepmc +1 more source
Several simulation techniques are used to explore static and dynamic behavior in polyanion sodium cathode materials. The study reveals that universal machine learning interatomic potentials (MLIPs) struggle with system‐specific chemistry, emphasizing the need for tailored datasets.
Martin Hoffmann Petersen +5 more
wiley +1 more source
Toward a public health leadership national training agenda: a review of conceptual frameworks and core competencies. [PDF]
Burke EM +5 more
europepmc +1 more source
The Necessity of Dynamic Workflow Managers for Advancing Self‐Driving Labs and Optimizers
We assess the maturity and integration readiness of key methodologies for Materials Acceleration Platforms, highlighting the need for dynamic workflow managers. Demonstrating this, we integrate PerQueue into a color‐mixing robot, showing how flexible orchestration improves coordination and optimization.
Simon K. Steensen +6 more
wiley +1 more source
Context and generalizability in health policy and systems research: a plea for an integrative praxis of theorizing. [PDF]
Van Belle S, Marchal B.
europepmc +1 more source
A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai +8 more
wiley +1 more source
Can environmental signals influence dietary Behaviours?The impact of governmental green development attention on dietary diversity among older adults. [PDF]
Lei H, Chen M, Qu H, Li L, Xie M.
europepmc +1 more source

