Results 201 to 210 of about 1,725,284 (283)

When prediction fails: a computational complexity view of stress. [PDF]

open access: yesFront Vet Sci
Budaev S, Lai F, Morgan R, Rønnestad I.
europepmc   +1 more source

3D‐Printed Titanium Gyroid Scaffold Structure Integrated With Tough Hybrid Materials for Cartilage Replacement

open access: yesAdvanced Engineering Materials, EarlyView.
This study proposes a potential device design for joint cartilage replacement. Silica‐polytetrahydrofuran (SiO2‐PolyTHF) hybrids with customizable mechanical properties were developed to mimic the characteristics of a natural meniscus. These were synthesized through a two‐pot sol–gel hybrid process.
Yu‐Chien Lin   +12 more
wiley   +1 more source

Algorithmic Complexity in Textile Patterns

open access: yes
Algorithmic complexity, also called Kolmogorov complexity and Kolmogorov-Chaitin complexity, motivates the use of techniques to approximate the complexity of objects and measure similarity between them.
Metzler, Heidi
core  

Rotary 3D Printing With Integrated Electroplating

open access: yesAdvanced Engineering Materials, EarlyView.
A rotary material extrusion platform integrates localized copper electroplating with printing and encapsulation to fabricate cylindrical polymer–metal structures containing fully embedded, low‐resistance conductive pathways that enable internal Joule heating and thermally activated shape‐memory responses.
Antonio Zagaria   +5 more
wiley   +1 more source

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

open access: yesAdvanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin   +14 more
wiley   +1 more source

Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning

open access: yesAdvanced Engineering Materials, EarlyView.
Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose   +7 more
wiley   +1 more source

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