Results 141 to 150 of about 120,389,537 (218)
How AI Can Advance Mathematical Biology: Opportunities, Challenges, and Future Directions. [PDF]
Rabiei K +3 more
europepmc +1 more source
Evolution of Physical Intelligence Across Scales
By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu +7 more
wiley +1 more source
Rhizosphere and Soil Depth Differentially Shape Microbial Community Composition and Assembly in <i>Phragmites australis</i> Salt-Marsh Soils. [PDF]
Wang L +8 more
europepmc +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Precision and Error Propagation in Static MEMS-IMU Inertial Navigation: A Stochastic Time-Series Analysis. [PDF]
Kariminejad MM +3 more
europepmc +1 more source
Autonomous AI‐Driven Design for Skin Product Formulations
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang +5 more
wiley +1 more source
Multidimensional Drivers of Phytoplankton Assembly in a Karst Reservoir: Seasonal Dynamics and Regulatory Implications. [PDF]
Yuan Z +7 more
europepmc +1 more source
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta +3 more
wiley +1 more source
S-shaped Utility Maximization with VaR Constraint and Partial Information. [PDF]
Zhu D, Davey A, Zheng H.
europepmc +1 more source
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary +1 more
wiley +1 more source

