Drivers of Vertical HIV Transmission in Sub-Saharan Africa and the Impact and Cost-Effectiveness of Targeted and Universal Lenacapavir Pre-Exposure Prophylaxis. [PDF]
Yakusik A +9 more
europepmc +1 more source
MODELING THE EVALUATION OF UNIVERSITY’S RATING BY MATHEMATICAL METHODS
openaire +1 more source
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy +8 more
wiley +1 more source
Population dynamics of generalist and specialist strategies under feast-famine cycles. [PDF]
Niimi R, Furusawa C, Himeoka Y.
europepmc +1 more source
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
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
Modeling the distribution of soil organic carbon in salt marshes dominated by various plant species along Egypt's Delta coast. [PDF]
Eid EM +3 more
europepmc +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
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
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
Evolution's hidden architecture: a non-lipschitz theory of creation and catastrophe. [PDF]
Durán-Olivencia MA.
europepmc +1 more source

