Results 101 to 110 of about 203,799 (303)
Letter from Jim R., Anniston, Alabama, to Allie Mae Edmondson, Heflin, Alabama, February 12, 1918
Letter from Jim R.
R., Jim
core
An Integrated NLP‐ML Framework for Property Prediction and Design of Steels
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
Letter from C. E. Y., Memphis, Tennessee, to Allie Mae Edmondson, October 19, 1919
Letter from C.E.Y.
C. E. Y.
core
Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials
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
Letter from Mae Wynne McFarland to Jack E. Josey
A typed letter to Jack E. Josey from Mae Wynne McFarland, discussing the validity of historical information from 'old Uncle Jeff' concerning Sam Houston's 'Steamboat house'
McFarland, Mae Wynne
core
ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals
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
Background Early childhood development metrics remain substandard among marginalized ethnic minority communities in three border provinces of Thailand. This study evaluated the levels and determinants of health literacy (HL) among primary caregivers to ...
Nathamon Wutthipan +7 more
doaj +1 more source
Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao +9 more
wiley +1 more source
MAE-6226: Aerodynamics Course Syllabus
MAE-6226 (Aerodynamics) Course Syllabus, as taught at the George Washington University in Spring 2017.GW University Bulletin 2016-2017: http://bulletin.gwu.edu/search/?P=MAE ...
Lorena A. Barba (97553)
core +1 more source
Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li +6 more
wiley +1 more source

