Results 161 to 170 of about 3,952,319 (243)
Quantifying cortex-wide traveling brain waves of complex patterns with a graph-based algorithm. [PDF]
Ho KT, Onoe H, Isa T, Chen CY.
europepmc +1 more source
Signed Edge Total Domination on Rooted Product Graphs
C. Shobha Rani +2 more
openaire +1 more source
A new data‐efficient framework combining DFT calculations, a neural network model, and automated graph analysis of catalytic reaction networks is proposed and applied to CO2 hydrogenation on transition metal nanoparticles. The analysis shows how efficient C2 oxygenate production requires a balance between CHx formation, C–C coupling, protonation, and ...
Mikhail V. Polynski, Sergey M. Kozlov
wiley +1 more source
Large language model enhanced public opinion monitoring for China's portable energy market: A data driven analysis of social media comments. [PDF]
Zhang Y +6 more
europepmc +1 more source
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su +13 more
wiley +1 more source
Editorial: Intelligent assistants for all. [PDF]
Mandischer N, Kraus M, Zhong J, Tapus A.
europepmc +1 more source
Recent Advances in Carbon Cathode Materials toward High‐Performing Zinc‐Ion Hybrid Supercapacitors
Aqueous zinc‐ion hybrid supercapacitors (ZHSCs) have emerged as promising next‐generation electrochemical energy‐storage devices. Porous carbon materials have been extensively studied as cathodes, with advances in tailoring pore structures and material properties to enhance performance.
Take Hirooka +3 more
wiley +1 more source
Identification and characterization of R75estA, a halophilic polyester-degrading enzyme from a marine bacterium. [PDF]
Ruginescu R +5 more
europepmc +1 more source
A Generative Neuro‐Symbolic AI for Protein Sequence Design
We introduce EffieDes, a neuro‐symbolic framework coupling deep learning‐based fitness landscape parameterization with exact automated reasoning. Unlike greedy sampling, EffieDes identifies sequences that globally optimize fitness while satisfying intricate design constraints.
Marianne Defresne +12 more
wiley +1 more source

