The development of social learning: from pedagogical cues to selective learning. [PDF]
Ishikawa M, Itakura S.
europepmc +1 more source
This review highlights recent advances in label‐free optical biosensors based on 2D materials and rationally designed mixed‐dimensional nanohybrids, emphasizing their synergistic effects and novel functionalities. It also discusses multifunctional sensing platforms and the integration of machine learning for intelligent data analysis.
Xinyi Li, Yonghao Fu, Yuehe Lin, Dan Du
wiley +1 more source
Cognitive-behavioral therapy to normalize social learning for patients with major depressive disorders: study protocol for a single-arm clinical trial. [PDF]
Jin Y +11 more
europepmc +1 more source
This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva +9 more
wiley +1 more source
A framework for the emergence and analysis of language in social learning agents. [PDF]
Wieczorek TJ +3 more
europepmc +1 more source
Liquid Metals as Initiators of Free‐Radical Polymerization of Hydrogels: A Perspective
Gallium‐based liquid metals initiate free radical polymerization to form hydrogels without the use of toxic molecular initiators. In addition to initiating polymerization, they can act as crosslinkers, yielding softer, more extensible, and tougher hydrogels than those formed with conventional initiators.
Syed Ahmed Jaseem +8 more
wiley +1 more source
Dynamic strategic social learning in nest-building zebra finches and its generalisability
Breen AJ +7 more
europepmc +1 more source
The social learning account of trypophobia. [PDF]
Cole GG, Millett AC, Juanchich M.
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
Wanting to matter and learning to care: A neurodevelopmental window of opportunity for (Pro) social learning? [PDF]
Dahl RE +2 more
europepmc +1 more source

