Results 191 to 200 of about 195,223 (244)

Magneto-Tunable Surface Roughness and Hydrophobicity of Magnetoactive Elastomers Based on Polymer Networks with Different Architectures. [PDF]

Polymers (Basel)
Kirgizov SE, Kostrov SA, Kramarenko EY.
europepmc   +1 more source

Fine-Tuning Directional Message Passing Neural Networks: Predicting Properties of Conjugated Organic Polymers with High Accuracy. [PDF]

Polymers (Basel)
Koskin IP, Petrosyan LS, Kazantsev MS.
europepmc   +1 more source

Mães de crianças prematuras: vinculação e psicopatologia [PDF]

, 2012
Bernardo, M., Campos, J., Gesta, C.
core  

The impact of prior level of care on the course of proximal humeral fractures in older patients: an analysis based on health insurance claims data. [PDF]

BMC Health Serv Res
Iking J, Katthagen JC, Koeppe J, Fischhuber K, Happe JP, Marschall U, Raschke MJ, Stolberg-Stolberg J.   +7 more
europepmc   +1 more source

Learning potential energy surfaces of hydrogen atom transfer reactions in peptides.

Digit Discov
Neubert M, Reiser P, Gräter F, Friederich P.   +3 more
europepmc   +1 more source

Smoking status among hypertensive patients in Mosul City. [PDF]

J Educ Health Promot
Dawood OT, Alkhyatt MM.
europepmc   +1 more source

Prediction of Properties of Polymer Composite Formulations Using Ensemble Models With Feature Generation

Journal of Applied Polymer Science, EarlyView.
This study develops an interpretable machine‐learning framework to predict multiple properties of polymer composites based on composition and processing variables. By combining ensemble models with composition‐based feature generation and SHAP‐based interpretation, the approach reveals composition‐property relationships and supports efficient multi ...
Dong Ryeol Shin, Sung Kwang Lee
wiley   +1 more source

Use of TaqMan array card to investigate bacterial isolates in diabetic foot ulcers. [PDF]

J Med Microbiol
Johnson-Lynn S, Curran MD, Allen C, Webber KM, Maes M, Routledge M, Enoch DA, Robinson AHN, Coll AP.   +8 more
europepmc   +1 more source

A Hybrid Semi‐Inverse Variational and Machine Learning Approach for the Schrödinger Equation

Advanced Physics Research, EarlyView.
A hybrid semi‐inverse variational and machine‐learning framework is presented for solving the Schrödinger equation with complex quantum potentials. Physics‐based variational solutions generate high‐quality training data, enabling Random Forest and Neural Network models to deliver near‐perfect energy predictions.
Khalid Reggab, Houssam Eddine Hailouf, Kingsley Onyebuchi Obodo, Mireille Megnidio‐Tchoukouegno, Mohammed Benali Kanoun, Souraya Goumri‐Said   +5 more
wiley   +1 more source

Measurement Error and Power in Family-Based Extensions to Mendelian Randomization. [PDF]

Behav Genet
Castro-de-Araujo LFS, Singh M, Zhou YD, Vinh P, Maes HHM, Verhulst B, Dolan CV, Neale MC.   +7 more
europepmc   +1 more source