Results 141 to 150 of about 1,934,499 (254)

On the Road: A Summer Odyssey in Dixie

open access: yes, 2015
All summer long, readers of The Gettysburg Compiler were treated to posts from Pohanka interns documenting their research and experiences at historical sites across the country. While I did not participate in the Pohanka internship program this summer, I
Lauck, Jeffrey L.
core  

AI in chemical engineering: From promise to practice

open access: yesAIChE Journal, EarlyView.
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

Reaction kinetics model in liquid and solid phases and its parameterization for room temperature sodium–sulfur battery

open access: yesAIChE Journal, EarlyView.
Abstract A multipore, multiphase, continuum model is assembled for the first time for room temperature sodium–sulfur (RT Na–S) batteries, with Na+ ion transport and redox reactions in the liquid electrolyte phase and semisolid phase of precipitates softened by the electrolyte solvent, as guided by molecular dynamics simulations in this study ...
Hakeem A. Adeoye   +3 more
wiley   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

open access: yesAdvanced Intelligent Discovery, EarlyView.
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

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

open access: yesAdvanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang   +4 more
wiley   +1 more source

Facilitators and barriers to the formulation of public policies on food and nutrition: A scoping review. [PDF]

open access: yesPLOS Glob Public Health
Feldenheimer da Silva AC   +9 more
europepmc   +1 more source

Smart Bioinspired Material‐Based Actuators: Current Challenges and Prospects

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This work gathers, in a review style, an extensive and comprehensive literature overview on the development of autonomous actuators based on synthetic materials, bringing together valuable knowledge from several studies. Furthermore, the article identifies the fundamental principles of actuation mechanisms and defines key parameters to address the size
Alejandro Palacios   +4 more
wiley   +1 more source

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