Results 171 to 180 of about 1,208 (288)

Solitary and soliton solutions of the nonlinear fractional Chen Lee Liu model with beta derivative. [PDF]

open access: yesSci Rep
Hussain A   +5 more
europepmc   +1 more source

A Single‐Cell Transcriptomic Atlas of the Ovine Rumen Microbiome Characterizes Lineage‐Specific Metabolic Shifts Associated with Host Heat Tolerance

open access: yesAdvanced Science, EarlyView.
An optimized single‐cell transcriptomic framework profiles over 60 000 cells to map the ovine rumen microbiome, partitioning the ecosystem into seven cross‐species functional clusters. In heat‐resistant hosts, a lineage‐specific metabolic shift in Anaerovibrio lipolyticus toward a highly glycolytic phenotype contributes to a “nutritional sparing ...
Sanbao Zhang   +8 more
wiley   +1 more source

Temporal interface in dispersive hyperbolic media. [PDF]

open access: yesNanophotonics
Ptitcyn G   +3 more
europepmc   +1 more source

Interacting Parallel Fluidic Hysterons

open access: yesAdvanced Science, EarlyView.
The parallel coupling of fluidic hysterons is introduced, establishing advanced functionalities in inflatable soft systems. A pressure–volume framework reveals how preset volumes Δv∗${\Delta }v^*$ tune nonlinear interactions between hysterons and actuation sequences without changing architecture. Experiments validate the predictions, opening new routes
Katrien Stinissen   +2 more
wiley   +1 more source

Physics‐Informed Neural Network‐Enabled Forward Prediction and Inverse Design of Ring Origami

open access: yesAdvanced Science, EarlyView.
This work presents a KRT‐PINN framework that integrates Kirchhoff rod theory with physics‐informed neural networks for the forward prediction and inverse design of ring origami consisting of closed‐loop rods. The framework predicts stable states of segmented rings with prescribed natural‐curvature profiles and determines the natural‐curvature profiles ...
Luyuan Ning   +3 more
wiley   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

open access: yesAdvanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai   +3 more
wiley   +1 more source

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