Results 191 to 200 of about 2,775,395 (241)

Rod Origami (RodOri) Spring Metamaterials for Tunable Vibration Control via Tailored Structural Instabilities

open access: yesAdvanced Science, EarlyView.
Rod Origami (RodOri) springs harness snap‐through instabilities of pre‐stressed curved rods with geometrically programmable buckling behavior. Integrating RodOri springs with distinct geometries yields multistable metamaterials that undergo stepwise reconfiguration via sequential snapping, achieving wide‐range tunability of stiffness and resonance ...
Jeseung Lee   +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

HMGCR‐Driven Cholesterol Metabolism Promotes Osteoarthritis Progression by Accelerating Synovial Fibroblast Senescence

open access: yesAdvanced Science, EarlyView.
In the pathological context of osteoarthritis (OA), the phosphorylation of AKT1 at Ser473 enhances its binding to Lys140 of Insig1, which facilitates the formation of AKT1–Insig1 complex. Subsequently, the activation of AKT1 promotes the phosphorylation of Insig1 at Ser189, potentially enhancing the dissociation of Insig1 from sterol regulatory element‑
Xiaoqi Zhang   +19 more
wiley   +1 more source

Origami Metamaterials Based on Low‐Melting‐Point Alloy Phase Transition: Breaking the Trade‐Off Between Reusability and Energy Absorption Quality

open access: yesAdvanced Science, EarlyView.
This work presents an origami metamaterial that integrates a low‐melting‐point alloy skeleton into an elastomeric shell. It dissipates energy through the metal's plastic deformation and recovers through the solid–liquid phase transition of the alloy together with the hyperelasticity of the shell.
Yupeng Liu   +5 more
wiley   +1 more source

Prediction of Poisson's ratio for a petroleum engineering application: Machine learning methods. [PDF]

open access: yesPLoS One
Alakbari FS   +6 more
europepmc   +1 more source

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