Results 81 to 90 of about 4,718 (211)
Multiscale higher-order molecular simplicial complex embedding for drug response prediction. [PDF]
BioinformaticsShen C, Lin G, Hu CS, Luo J.
europepmc +1 more source
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Advanced Intelligent Discovery, EarlyView.Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
Interaction topology theory deciphers multiscale codes of MOF-like materials. [PDF]
Sci AdvChen D, Liu J, Chen CL, Wei GW.
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
Topological potentials guiding protein self-assembly. [PDF]
PLoS Comput BiolSpirandelli ILA +3 more
europepmc +1 more source
本文立足于东方哲学道生一的本体论思想,提出太极统一场论的数理公设。核心洞察在于:将宇宙的绝对基底定义为数学上的-1,它不仅是真空极化的背景场,更在数学物理上等价于所有基本物理常数(光速c、普朗克常数h、精细结构常数α等)的总约束项。可观测的物质与能量被视为在此刚性基底上产生的扰动波函数Ψ。在此框架下,本文重构了概率幅演化公式,统一解释了量子概率归一化、宏观引力几何化,并指出物理常数是宇宙拓扑结构的必然产物。本文结合代数拓扑基本群、素数太极场和广义相对论,给出了跨学科印证,并提出了具体的实验验证路径,展示了东方系统思想与现代科学统一的巨大潜力。 Based on the ontological thought of Dao generates One from Eastern ...
openaire +2 more sources
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
Robust point cloud lightweighting with multi-scale adaptive filtering and entropy-driven subdivision. [PDF]
PLoS OneZeng W, Gao X, Lu Q, Zhu N, Li M, Cai W.
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source