Results 181 to 190 of about 280,801 (322)

The Frequency‐Domain Lattice Boltzmann Method (FreqD‐LBM): A Versatile Tool to Predict the QCM Response Induced by Structured Samples

open access: yesAdvanced Theory and Simulations, EarlyView.
FreqD‐LBM simulates the oscillatory flow at the surface of a QCM‐D resonator in the presence of structured adsorbates. It derives shifts of frequency and bandwidth (equivalent to dissipation) on different overtones. Applications include rough surfaces, adsorbed rigid particles, adsorbed viscoelastic particles, spheres floating freely above the surface,
Diethelm Johannsmann   +5 more
wiley   +1 more source

Exploration of Half‐Cycle Length of Converging Circular Wavy Duct with Diverging‐Outlet: Turbulent Water Dynamics

open access: yesAdvanced Theory and Simulations, EarlyView.
Increasing half‐cycles intensifies turbulence due to enhanced vortex interactions and flow separation at the diverged‐outlets. Longer wavy ducts are shown to increase flow acceleration, resulting in greater output velocities and more turbulent‐kinetic‐energy production. Wave‐period plays a crucial role in determining turbulent intensity, with amplitude
I. L. Animasaun   +2 more
wiley   +1 more source

Establishing Convergent Validity of the FACE-Q Aesthetics Module Scales. [PDF]

open access: yesAesthet Surg J
Gallo L   +8 more
europepmc   +1 more source

Asymmetric Inter‐Hemisphere Communication Contributes to Speech Acquisition of Toddlers with Cochlear Implants

open access: yesAdvanced Science, EarlyView.
The present study constructs machine‐learning models to resolve developmental relationships among auditory performance, auditory cortical processing, and functional connectivity of the bilateral language network during the first year of restored hearing. The results demonstrate that asymmetric inter‐hemisphere communication contributes significantly to
Xue Zhao   +6 more
wiley   +1 more source

Ultralow‐Dimensionality Reduction for Identifying Critical Transitions by Spatial‐Temporal PCA

open access: yesAdvanced Science, EarlyView.
The proposed spatial‐temporal principal component analysis (stPCA) method analytically reduces high‐dimensional time‐series data to a single latent variable by transforming spatial information into temporal dynamics. By preserving the temporal properties of the original data, stPCA effectively identifies critical transitions and tipping points.
Pei Chen   +6 more
wiley   +1 more source

Home - About - Disclaimer - Privacy