Results 241 to 250 of about 1,152,162 (330)
Utilization of Ontology to Develop Artificial Intelligence Systems in the Healthcare Industry. [PDF]
Parsanasab E, Ahmadipour A, Mehraeen E.
europepmc +1 more source
A frequency‐tunable ferroelectric synaptic transistor based on a buried‐gate InGaZnO channel and Al2O3/HfO2 dielectric stack exhibits linear and reversible weight updates using single‐polarity pulses. By switching between ferroelectric and trap‐assisted modes depending on input frequency, the device simplifies neuromorphic circuit design and achieves ...
Ojun Kwon +8 more
wiley +1 more source
Editorial: Prompts: the double-edged sword using AI. [PDF]
Vallverdú J, Rzepka R, Sans Pinillos A.
europepmc +1 more source
Herein presented supraparticles combine the nanoparticulate photocatalyst graphitic carbon nitride with the enzyme horseradish peroxidase, which is immobilized on silica nanoparticles. In an optimized compatibility range, both catalysts operate effectively within the hybrid supraparticles and catalyze a cascade reaction consisting of the photocatalytic
Bettina Herbig +11 more
wiley +1 more source
Foundations and practices of unified modeling language: J.UCS special issue [PDF]
Kim, Dae-Kyoo, Trujillo, Juan
core
Advancing large-molecule discovery with a unified digital platform for data analysis and workflow management. [PDF]
Natali E +3 more
europepmc +1 more source
U-SAM: An audio language Model for Unified Speech, Audio, and Music Understanding [PDF]
Ziqian Wang +3 more
openalex +1 more source
A programmable interpenetrating double‐network architecture, created via 3D‐TIPS printing and resin infusion, synergistically combines thermoplastic and thermosetting elastomers to balance structural rigidity and surface softness—crucial for paediatric laryngeal stents.
Elizabeth F. Maughan +14 more
wiley +1 more source
A multistream attention based neural network for visual speech recognition and sign language understanding. [PDF]
Talaat FM, Hassan BM.
europepmc +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source

