Results 151 to 160 of about 8,736,022 (255)

Hierarchical Multi‐Material Architectures With Gradient Design for Dynamic‐Range Flexible Tactile Sensing

open access: yesAdvanced Materials Technologies, EarlyView.
Hierarchical multi‐material TPMS lattices are engineered as flexible tactile sensors by combining soft and stiff elastomeric layers with a conformal conductive coating. The bilayer architecture delivers sensitivity at low pressures while maintaining a broad detectable range under large loads, enabling reliable pressure and vibration monitoring for ...
Reza Noroozi   +3 more
wiley   +1 more source

Advanced Manufacturing of Composite‐Based Systems for Energy Applications

open access: yesAdvanced Materials Technologies, EarlyView.
Advanced manufacturing enables the integration of polymers, ceramics, metal oxides, and composites into architected microstructures with tailored transport pathways. By coupling material selection, manufacturing strategy, and structural design, multifunctional energy systems can simultaneously improve electrochemical performance, thermal management ...
Sri Vaishnavi Thummalapalli   +13 more
wiley   +1 more source

Latent mixed-effect models for high-dimensional longitudinal data

open access: yes
Publisher Copyright: © 2025, Transactions on Machine Learning Research. All rights reserved.Modelling longitudinal data is an important yet challenging task.
Lönnroth, Otto   +3 more
core  

Sentiment Analysis of Acceptance TVET Online Courses on the Skill Academy App from Google Play: Leveraging Text Mining with Comparison Machine Learning Model. [PDF]

open access: yesF1000Res
Darmono D   +8 more
europepmc   +1 more source

Directional Latent Hybridization: Beyond Random Noise in Physics‐Informed Generative Inverse Design of Nonlinear Metamaterials

open access: yesAdvanced Materials Technologies, EarlyView.
A physics‐informed generative framework introduces Directional Latent Hybridization (DLH) for the deterministic inverse design of nonlinear metamaterials. By hybridizing dominant traits from parent geometries in the latent space, DLH overcomes the instabilities of stochastic models to ensure high structural precision at high densities.
Semin Ahn   +2 more
wiley   +1 more source

Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty

open access: yes
BACKGROUND: Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling ...
Machine Learning Consortium
core  

Low‐Pressure Plasma‐Based Wrinkling of PDMS and Machine Learning‐Driven Property Engineering

open access: yesAdvanced Materials Technologies, EarlyView.
Wrinkled surfaces are well‐suited for controlled surface deformations in the µm range. The key challenge is the relation between the resulting wrinkle features and the necessary process conditions. Machine learning techniques have solved the prediction and inverse design problems for various preparation conditions, opening a precisely controlled ...
Fabian Kopsch   +7 more
wiley   +1 more source

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