Results 221 to 230 of about 2,363,364 (342)
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi +5 more
wiley +1 more source
Entorhinal Silencing Reveals Energy Cascade Organization of Hippocampal Oscillations. [PDF]
Zhao B, Qin Y, Burke SN, Maurer AP.
europepmc +1 more source
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia +3 more
wiley +1 more source
Vibrational Energy Harvesting via Phase Modulation: Effects of Different Excitations. [PDF]
Adesina PO, Vincent UE, Kolebaje OT.
europepmc +1 more source
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
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
Real-time PQD detection classification and localization using recurrence plots and EfficientNet-SE in solar integrated IEEE 13-bus system. [PDF]
Nasika D, Varadharaj EK, M MR, J K.
europepmc +1 more source
A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam +5 more
wiley +1 more source
Power quality optimization framework for three phase microgrids with grid tied solar PV and battery storage under nonlinear loads. [PDF]
Saleh Waseem Abbasi M +5 more
europepmc +1 more source
This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu +5 more
wiley +1 more source
A radar vital signs detection method in complex environments. [PDF]
Zhang C +5 more
europepmc +1 more source

