Results 141 to 150 of about 4,867 (233)

AI in chemical engineering: From promise to practice

AIChE 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, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Scientific opinion on the amendment of the specifications for vegetable carbon (E 153) as a food additive. [PDF]

EFSA J
EFSA Panel on Food Additives and Flavourings (FAF), Castle L, Andreassen M, Aquilina G, Bastos ML, Boon P, Fallico B, FitzGerald R, Frutos Fernandez MJ, Grasl-Kraupp B, Gundert-Remy U, Gürtler R, Houdeau E, Kurek M, Louro H, Morales P, Passamonti S, Furst P, Gaffet E, Loeschner K, Mast J, Mirat M, Oomen A, Undas A, Mech A, Smeraldi C, Rincon AM.   +26 more
europepmc   +1 more source

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

Advanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod, Ajay Vikram Singh, Preeti Bhardwaj, Tulika Rai, Sweta Vijay Nakhale, Amruta Shelar, Rajendra Patil, Peter Laux, Andreas Luch, Christopher J. Osgood, Michael W. Stacey   +10 more
wiley   +1 more source

The effect of forest policy on the use of forest resources and forest industry investments in Russia [PDF]

, 2013
Gerasimov, Y., Jutila, L., Karjalainen, T., Leinonen, T.   +3 more
core  

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
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

Probing the complexity of wood with computer vision: from pixels to properties. [PDF]

J R Soc Interface
Lukovic M, Ciernik L, Müller G, Kluser D, Pham T, Burgert I, Schubert M.   +6 more
europepmc   +1 more source

Establishment of suitable reference genes for studying relative gene expression during the transition from trophozoites to cyst-like stages and first evidences of stress-induced expression of meiotic genes in Trichomonas vaginalis. [PDF]

Parasitology, 2021
Peixoto JF, Dos Santos DG, Ribeiro L, de Oliveira VSC, Nunes-da-Fonseca R, Nepomuceno-Silva JL.   +5 more
europepmc   +1 more source

Metsätalouden kansantulo-osuuden laskenta. [PDF]

, 1971
Kunnas, Heikki J
core