Results 201 to 210 of about 106,916 (291)

Generative Artificial Intelligence in Clinical Practice: How Should Rheumatologists Evaluate and Monitor Emerging Tools?

open access: yesArthritis Care &Research, EarlyView.
Generative artificial intelligence (AI) is increasingly being used in rheumatology practice, to assist with tasks such as documentation, record summarization, and clinical decision support. Rheumatologists need a practical approach to determining what evidence and oversight each application requires.
Koushan Kouranloo   +2 more
wiley   +1 more source

Air‐Pressure–Actuated Vibroacoustic Metamaterial With Tunable Bandgap: Design, Modeling, and Characterization

open access: yesAdvanced Engineering Materials, EarlyView.
This article presents the design, modeling, and characterization of air‐pressure–actuated programmable vibroacoustic metamaterials (PVAMM). The study focuses on leveraging air pressure to dynamically tune resonance frequencies for effective noise attenuation.
William Kaal   +2 more
wiley   +1 more source

Comparative Performance of Electrode Models for Thickness-Shear AT-Cut Quartz Resonators. [PDF]

open access: yesMicromachines (Basel)
Chen H   +7 more
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

Investigation of Resonance Characteristics of More Than 10 000 Metal‐Coated Silicon MEMS Cantilevers Using Machine Learning

open access: yesAdvanced Engineering Materials, EarlyView.
More than 10 000 metal‐coated silicon MEMS cantilevers are fabricated and characterized to build a large‐scale experimental dataset spanning multiple vibrational modes. Machine learning algorithms are benchmarked to predict resonance frequency and quality factor, with ensemble based models achieving accuracy approaching experimental uncertainty.
Sina Zare Pakzad   +8 more
wiley   +1 more source

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