Results 241 to 250 of about 24,866 (298)

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

A detailed analysis of the Czernik 38 cluster and its associated tidal tail, utilizing Gaia DR3 and 2MASS. [PDF]

Sci Rep
Ahmed NM.
europepmc   +1 more source

Implantation‐On‐Chip: An AI‐Based Platform for Monitoring the Embryo Trophoblast–Endometrial Stroma Cross Talk With Xenobiotics Interference

Advanced Intelligent Systems, EarlyView.
We present a novel AI‐integrated implantation‐on‐chip platform that enables mimicking and monitoring the maternal–fetal interactions at the early phases of human embryo implantation with high spatiotemporal resolution. The complexity of the trophoblast invasion process was addressed by conducting the analysis at global (rate of invasion) and local ...
Joanna Filippi, Valentina Lacconi, Arianna Mencattini, Micol Massimiani, Dolores Lobresca, Gaia Posanzini, Alessia Riccardi, Paola Casti, Michele D’Orazio, Gianni Antonelli, Giorgia Curci, Luisa Campagnolo, Eugenio Martinelli   +12 more
wiley   +1 more source

Using a neural network approach and starspots dependent models to predict effective temperatures and ages of young stars. [PDF]

PLoS One
Tarantino M, Prisinzano L, D'Angelo N, Damiani F, Adelfio G.   +4 more
europepmc   +1 more source

Data‐Driven Design of Bimodal Networked Dielectric Elastomers for High‐Performance Artificial Muscles

Advanced Intelligent Systems, EarlyView.
A data‐efficient artificial intelligence‐assisted framework, which integrates experimental data with machine learning, is developed for the design of bimodal networked dielectric elastomers (DEs) as advanced artificial muscles. It adopts neural networks to predict DEs’ mechanical properties and support vector machines to classify electromechanical ...
Ofoq Normahmedov, Hanzhi Ma, Junbo Peng, Huifeng Dong, Jiangshan Zhuo, Mengke Shi, Lvting Wang, Shengchao Jiang, Ye Shi   +8 more
wiley   +1 more source

The Progenitor Systems of Classical Novae in M31. [PDF]

Astrophys J
Abelson CS, Badenes C, Chomiuk L, Williams BF, Breivik K, Galbany L, Jiménez-Palau C.   +6 more
europepmc   +1 more source

Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy

Advanced Intelligent Systems, EarlyView.
Hallgrimson et al. introduce a machine learning algorithm, siMILe, that takes features of single‐molecule localization microscopy localization clusters (e.g., size and sphericity) and finds the clusters that are associated with certain cell conditions (such as differential protein expression or drug treatment).
Christian Hallgrimson, Y. Lydia Li, Claire A. Shou, Ben Cardoen, John Lim, Timothy H. Wong, Ismail M. Khater, Ivan Robert Nabi, Ghassan Hamarneh   +8 more
wiley   +1 more source

3D STED Super-Resolution Imaging Strategy for Visualizing Synaptic Nano-architecture in Brain Cryosections. [PDF]

Bio Protoc
Scripter J, Skeens A, Jones G, Akter Y, Hruska M.   +4 more
europepmc   +1 more source

i‐Tac: Inverse Design of 3D‐Printed Tactile Elastomers with Tuneable Optical and Mechanical Properties

Advanced Intelligent Systems, EarlyView.
Inspired by the multi‐tissue architecture of the human fingertip dermis (A), this work introduces a mixture design using three PolyJet materials (AC/TM/GM) to expand the achievable elastomer property space (B). An inverse design pipeline (i‐Tac) is developed to map target optical/mechanical requirements to optimal material compositions (C), enabling ...
Wen Fan, Dandan Zhang
wiley   +1 more source

Bacterial specificity of the gut microbiome predicts bone density in primary hyperparathyroidism. [PDF]

Bone Res
Dar HY, Fang J, Patil S, Roy NK, Agarwal S, Weitzmann MN, Jones RM, Bilezikian JP, Pacifici R.   +8 more
europepmc   +1 more source