Results 261 to 270 of about 1,801,897 (347)

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

MultiFixRadSoft: A Comprehensive Tool for Primary Relative Radiometric Scale Realization in Radiation Thermometry. [PDF]

Sensors (Basel)
Ertürk M, Karabulut M, Kadı ÖF, Gözönünde C, Broberg P, Olsen ÅAF, Nasibli H.   +6 more
europepmc   +1 more source

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

Mathematical modeling and statistical analysis of breast cancer drugs using M-polynomial indices for the physical properties.

Sci Rep
Tawhari QM, Naeem M, Maqbool S, Rauf A, Oyelakin O.   +4 more
europepmc   +1 more source

A design of multiple color image encryption scheme based on finite algebraic structures. [PDF]

Sci Rep
Qayyum T, Shah T, Khan I, Popa IL.
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Machine-Learning Interatomic Potentials Achieving CCSD(T) Accuracy for Systems with Extended Covalent Networks and van der Waals Interactions. [PDF]

J Chem Theory Comput
Ikeda Y, Forslund A, Kumar P, Ou Y, Jung JH, Köhn A, Grabowski B.   +6 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
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

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
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, Nicholas Ballard   +1 more
wiley   +1 more source

Growth strategies across life-history stages and generational turnover of cryptobenthic coral reef fishes of the genus Trimma. [PDF]

J Fish Biol
Goldsworthy NC, Srinivasan M, Smallhorn-West P, Jones GP.   +3 more
europepmc   +1 more source