Results 201 to 210 of about 2,921,645 (302)

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

open access: yesAdvanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju   +5 more
wiley   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

open access: yesAdvanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan   +8 more
wiley   +1 more source

Specific Random Trees for Random Forest

open access: yesIEICE Transactions on Information and Systems, 2013
LIU, Zhi, SUN, Zhaocai, WANG, Hongjun
openaire   +3 more sources

Co‐Delivery of Sustained Release Chondroitinase ABC‐37 With Human iPSC‐Derived Neural Progenitors Promotes Transplant Survival and Functional Recovery in a Rodent Model of Stroke

open access: yesAdvanced Science, EarlyView.
A regenerative strategy for stroke combines human stem cell–derived neural progenitor cells with sustained release of a matrix‐modifying, thermostable chondroitinase ABC‐37 enzyme. In a rat model of stroke, co‐delivery enhanced transplanted cell survival and neuronal differentiation, degraded inhibitory extracellular matrix components, and improved ...
Nitzan Letko Khait   +7 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

open access: yesAdvanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray   +3 more
wiley   +1 more source

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