Inspired by origami/kirigami, three-dimensional (3D) mesostructures assembled via a mechanics-guided approach, with reversible and maneuverable shape-morphing capabilities, have attracted great interest with regard to a broad range of applications. Despite intensive studies, the development of morphable 3D mesostructures with high-order (multi-degree-of-freedom) deformation and untethered high-frequency actuation remains challenging. This work introduces a scheme for a magnetically encoded transferable 3D mesostructure, with polyethylene terephthalate (PET) film as the skeleton and discrete magnetic domains as actuation units, to address this challenge. The high-order deformation, including hierarchical, multidirectional and blending shape morphing, is realized by encoding 3D discrete magnetization profiles on the architecture through ultraviolet curing. Reconfigurable 3D mesostructures with a modest structural modulus (∼3 GPa) enable both high-frequency (∼55 Hz) and large-deformation (∼66.8%) actuation under an alternating magnetic field. Additionally, combined with the shape-retention and adhesion property of PET, these 3D mesostructures can be readily transferred and attached to many solid substrates. On this basis, diverse functional devices, including a switchable colour letter display, liquid mixer, sequential flashlight and biomimetic sliding robot, are demonstrated to offer new perspectives for robotics and microelectronics.
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http://dx.doi.org/10.1093/nsr/nwac163 | DOI Listing |
Comput Biol Chem
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School of Computing and Information Technology, REVA University, Bengaluru, India.
Autism spectrum disorder (ASD) is the neuro-developmental disorder caused by various changes in the brain. It affects the life conditions with social interaction and communication. Most of the previous researches used the various techniques for the early detection to reduce the ASD, but it had been occurred several complications such as, time expenses, and low accessibility for diagnosis.
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Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Department of Neuroscience and Biomedical Engineering, Aalto University, Finland; Department of Computer Science, Aalto University, Finland.
Recent years have seen a growing interest in methods for predicting an unknown variable of interest, such as a subject's diagnosis, from medical images depicting its anatomical-functional effects. Methods based on discriminative modeling excel at making accurate predictions, but are challenged in their ability to explain their decisions in anatomically meaningful terms. In this paper, we propose a simple technique for single-subject prediction that is inherently interpretable.
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January 2025
FrontLab, Institut du Cerveau - Paris Brain Institute - ICM, INSERM, CNRS, Hôpital de la Pitié Salpêtrière, AP-HP, Sorbonne University, Paris, France.
Creative thinking involves the evaluation of one's ideas in order to select the best one, but the cognitive and neural mechanisms underlying this evaluation remain unclear. Using a combination of creativity and rating tasks, this study demonstrates that individuals attribute subjective values to their ideas, as a relative balance of their originality and adequacy. This relative balance depends on individual preferences and predicts individuals' creative abilities.
View Article and Find Full Text PDFNeuroimage
January 2025
Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA.
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January 2025
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