Results 281 to 290 of about 2,084,932 (380)
Identifying and predicting gait stability metrics in people with stroke in uneven-surface walking using machine learning. [PDF]
Inui Y, Takamura Y, Nishi Y, Morioka S.
europepmc +1 more source
Reevaluating the Activity of ZIF‐8 Based FeNCs for Electrochemical Ammonia Production
Though receiving much attention, the field of electrochemical nitrogen reduction reaction (eNRR) to ammonia is marked by doubts about whether this reaction is possible in aqueous media. This work sheds light on this question for iron single‐atom on N‐doped carbon (FeNC) catalysts—a class of well‐known catalysts that is also worth testing for the sister
Caroline Schneider +6 more
wiley +1 more source
The Role of Daily Stressors and Masculine Values on Mexican Adolescent Aggression. [PDF]
Espinosa-Hernandez G, Reich JC, Pond RS.
europepmc +1 more source
Implementation of Drug‐Induced Rhabdomyolysis and Acute Kidney Injury in Microphysiological System
A modular Muscle–Kidney proximal tubule‐on‐a‐chip integrates 3D skeletal muscle and renal proximal tubule tissues to model drug‐induced rhabdomyolysis and acute kidney injury. The coculture system enables dynamic tissue interaction, functional contraction monitoring, and quantification of nephrotoxicity, revealing drug side effect‐induced metabolic ...
Jaesang Kim +4 more
wiley +1 more source
Comparison of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) for different algorithms.
Jun Zhou (6477) +2 more
openalex +1 more source
A miniaturized mechano‐acoustic sensor is developed using an electrospun PVDF nanomesh as the diaphragm in a capacitive sensor structure. Unlike conventional nanomesh‐based sensors, it achieves high linear sensitivity, a broad and flat frequency response, and a compact form factor.
Jeng‐Hun Lee +8 more
wiley +1 more source
Gaussian dual adjacency graph based spatial correlated and temporal time dependent traffic prediction in Bangalore City. [PDF]
Ravichandran SK +4 more
europepmc +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source

