Results 181 to 190 of about 5,064,919 (256)
FedPome: federated deep learning for real-time pomegranate disease classification. [PDF]
S L +5 more
europepmc +1 more source
First supermodule of the MACRO detector at Gran Sasso
MACRO COLLABORATION, BELLOTTI, Roberto
core
Nanoparticle‐Mediated Bubble Suppression During Droplet Solidification for Mechanical Reinforcement
Nanoparticles mediate droplet solidification by raising nucleation temperature, refining dendrites, and slowing freezing‐front advance, thereby suppressing trapped air bubbles in droplet‐based 3D printing. This strategy reduces bubble volume fraction by ∼35% and increases compressive strength by up to 39%, offering a low‐cost route to stronger printed ...
Runmiao Gao +10 more
wiley +1 more source
Envirotyping-driven strategies to enhance soybean adaptation across Southern Africa. [PDF]
Fregonezi BF +15 more
europepmc +1 more source
The iLEAP technique integrates high‐throughput two‐photon lithography with extrusion printing, bridging sub‐micrometer precision and macroscopic scaffold fabrication. By employing a low‐exothermic photoinitiator, it mitigates thermal damage to biopolymers and preserves bioactivity.
Qifeng Guan +11 more
wiley +1 more source
Confidence and uncertainty aware deep learning for reliable grape leaf disease diagnosis under real world field conditions. [PDF]
Ergün E, Okumus H, Cinar OE, Batan N.
europepmc +1 more source
TSTScope is an interpretable AI framework that integrates single‐cell transcriptomes with TCR information through curated gene‐program constraints. By linking receptor context to functional T cell states, it reveals response‐associated tumor‐specific T cell programs in lung cancer immunotherapy cohorts and defines an MPR score associated with ...
Shiwei Cao +8 more
wiley +1 more source
PlantCLR: contrastive self-supervised pretraining for generalizable plant disease detection. [PDF]
Shah SSA +7 more
europepmc +1 more source
An attention‐based multimodal deep learning framework is developed to predict the creep life of Ni‐based superalloys by fusing processing parameters with microstructural micrographs. The model achieves high accuracy (R2 = 0.92), aligns with metallurgical principles by capturing δ‐phase influence, and incorporates uncertainty quantification, offering a ...
Haopeng Lv +10 more
wiley +1 more source

