Results 141 to 150 of about 4,813 (208)
In dynamic driving scenarios, the proposed approach ensures only temporally aligned sensor inputs to make driving decisions, preventing false activations. By enabling selective hardware‐level learning, it achieves fast, reliable responses under noisy conditions.
Kapil Bhardwaj +4 more
wiley +1 more source
Multimodal prediction of psychotic-like experiences using elastic net modeling: external validation in a clinical sample. [PDF]
Arslan S +7 more
europepmc +1 more source
Composite‐based flexible electronics integrate biodegradable polymers, conductive networks, and multilayer device architectures to balance electrical performance, mechanical durability, and controlled degradation. Interfacial engineering and encapsulation regulate transport stability and operational lifetime, while programmed disassembly enables ...
Sayam, Sangho Cho
wiley +1 more source
From data to decisions: a modular platform for modelling and simulation of infectious disease diffusion in networks. [PDF]
Branda F +7 more
europepmc +1 more source
Sulfide‐Based Electrolytes for All‐Solid‐State Sodium Batteries
This review covers the structural features and synthesis strategies of sulfide‐based solid electrolytes, as well as critical challenges related to conductivity, interfacial and moisture stability, and scaling‐up for practical application in Sodium‐based All Solid‐State Batteries.
Han Yang +6 more
wiley +1 more source
Anatomical phenotyping and staging of brain arteriovenous malformations. [PDF]
Beyersdorf B +6 more
europepmc +1 more source
Emerging Materials and Future Strategies for Solid Oxide Electrochemical Cells
Solid oxide electrochemical cells operate under strongly coupled electrochemical and thermodynamic conditions, where performance is constrained by interactions among crystal structure, defect chemistry, and interfacial evolution. This review, based on a structure‐defect‐property‐durability framework, reveals the roles of lattice symmetry and defect ...
Qiuchun Lu +4 more
wiley +1 more source
A structural machine learning approach for rapid prediction of thermodynamically destabilizing tyrosine phosphorylations. [PDF]
Woodard J +8 more
europepmc +1 more source
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park +19 more
wiley +1 more source

