Results 221 to 230 of about 472,437 (333)

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

open access: yesAdvanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu   +4 more
wiley   +1 more source

Reliability analysis of subsea pipeline system based on fuzzy polymorphic bayesian network. [PDF]

open access: yesSci Rep
Liu C   +9 more
europepmc   +1 more source

A Dual‐Ion Multiphysics Model for Smart and Sustainable Sensors Based on Bacterial Cellulose

open access: yesAdvanced Intelligent Systems, EarlyView.
Bacterial cellulose (BC), functionalized with ionic liquids (ILs) and conductive polymers, offers promise for sustainable sensor applications. To enable real‐world integration, this work presents the first dual‐carrier, multiphysics white‐box model of mechanoelectric transduction in BC–IL sensors, combining mechanical deformation and ion transport ...
Francesca Sapuppo   +7 more
wiley   +1 more source

gnSPADE: Incorporating Gene Network Structures Enhances Reference‐Free Deconvolution in Spatial Transcriptomics

open access: yesAdvanced Intelligent Systems, EarlyView.
gnSPADE integrates gene‐network structures into a probabilistic topic modeling framework to achieve reference‐free cell‐type deconvolution in spatial transcriptomics. By embedding gene connectivity within the generative process, gnSPADE enhances biological interpretability and accuracy across simulated and real datasets, revealing spatial organization ...
Aoqi Xie, Yuehua Cui
wiley   +1 more source

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