Results 191 to 200 of about 546,736 (277)

Interpreting artificial neural networks to detect genome-wide association signals for complex traits. [PDF]

open access: yesNAR Genom Bioinform
Yelmen B   +5 more
europepmc   +1 more source

Actuating and Sensing Composites of Liquid Crystal Elastomers and Poly(ionic liquid)s

open access: yesAdvanced Functional Materials, EarlyView.
Ionic conductive, mechanically tough, flexible, and stretchable filaments of a composite material comprising a liquid crystal elastomer and a poly(ionic liquid) are produced through 3D printing, which exhibit large actuation strain under stimulation and electrical resistance variation in response to deformation or environmental condition changes. Their
Zeping Liu   +6 more
wiley   +1 more source

Photon Avalanching Nanoparticles: The Next Generation of Upconverting Nanomaterials?

open access: yesAdvanced Functional Materials, EarlyView.
This Perspective outlines the mechanistic foundations that enable photon‐avalanche (PA) behavior in lanthanide nanomaterials and contrasts them with emerging application spaces and forward‐looking design strategies. By bridging threshold engineering, energy‐transfer dynamics, and materials engineering, we provide a coherent roadmap for advancing the ...
Kimoon Lee   +7 more
wiley   +1 more source

Does neurocognition predict personal recovery over time in psychotic disorder patients? [PDF]

open access: yesSchizophr Res Cogn
Rietveld R   +4 more
europepmc   +1 more source

Meniscus Pixel Printing for Contact‐Lens Vision Sensing and Robotic Control

open access: yesAdvanced Functional Materials, EarlyView.
A visual‐sensing contact lens is enabled by meniscus pixel printing (MPP), which rapidly patterns a 200 µm perovskite photodetector pixel in 1 s without masks, vacuum processing, or bulky equipment. A deep‐learning‐based super‐resolution reconstructs sparse on‐lens signals into 80 × 80 high‐resolution visual information, while AI‐driven eye‐tracking ...
Byung‐Hoon Gong   +7 more
wiley   +1 more source

BACH, a Bayesian Optimization Protocol for Accurate Coarse‐Grained Parameterization of Organic Liquids

open access: yesAdvanced Functional Materials, EarlyView.
We present a fully automated Bayesian optimization (BO) protocol for the parameterization of nonbonded interactions in coarse‐grain CG force fields (BACH). Using experimental thermophysical data, we apply the protocol to a broad range of liquids, spanning linear, branched, and unsaturated hydrocarbons, esters, triglycerides, and water.
Janak Prabhu   +3 more
wiley   +1 more source

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