Results 201 to 210 of about 25,178,133 (232)
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill +4 more
wiley +1 more source
Short-term and long-term adaptive changes in prosodic comprehension. [PDF]
Ostrow A, Kurumada C.
europepmc +1 more source
The dFoCC pipeline starts with observed DED and resting‐state coordinates, which are then used to generate a library of triggered states. Correlation analysis of the calculated DED features of each candidate vs observed DED permits quantitative evaluation of candidate structural quality.
Meng Iao Fong +3 more
wiley +1 more source
The Impact of Mimicry on Tobacco Addiction. [PDF]
Kulesza W +4 more
europepmc +1 more source
Abruptly changing from aerobic to anaerobic conditions (sudden anaerobization) induced growth inhibition and a significant increase in intracellular labile ferrous iron in the aerotolerant anaerobe Amphibacillus xylanus. We found that free flavins mediate efficient electron transfer from NADH to ferric iron under anaerobic conditions, suggesting that ...
Shinya Kimata +13 more
wiley +1 more source
New approach for health assessment of high voltage motor using experimental case studies. [PDF]
Al-Ameri SM +5 more
europepmc +1 more source
Assessment of a 40-year-old induction motor using hybrid diagnostic and AI-based predictive techniques. [PDF]
Butukuri KR +5 more
europepmc +1 more source
Trend-Conditioned Residual Learning for Early Fault Warning in Nonstationary Multi-Sensor Oil Monitoring. [PDF]
Li H, Chen Y, Wang Y, Wu C.
europepmc +1 more source
Exploring a comprehensive knowledge map for bridge management research: a Delphi-enhanced scientometric analysis. [PDF]
Peng P +5 more
europepmc +1 more source
Pavement condition prediction under small-sample conditions using a particle swarm optimization-based support vector machine. [PDF]
Xu W, Yang Z, Ji Y, Huang P.
europepmc +1 more source

