This Supporting Information includes information regarding the magnetic field of the actuator magnet, MR-LF-S (which has the same geometry as MR-LF and a soft compartment), and a table comparing MR-LF to other small-scale, flexible magnetic crawler robots.  Corresponding author email:   yong.kong@utah.edu

 This file includes: Figures S1 to S12Movies S1 to S10 Other Supplementary Materials for this manuscript include the following: Movies S1 to S10

This Supporting Information includes:Figure S1, S2, S3Supplementary VideosVideo S1: Process of self-folding of the SCS with 4 creases.Video S2: Process of self-folding of the SCS with 10 creases.        Video S3: Demonstration of stacking SCS with 10 sheets of paper. Video S4: Three-point bending test of the SCS.      

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Avi Hazan1, Elishai Ezra Tsur1*1 Neuro-Biomorphic Engineering Lab, Department of Mathematics and Computer Science, The Open University of Israel, Ra’anana, Israel* Correspondence: elishai@NBEL-lab.comAbstractNeuromorphic hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for machine learning. Here, we propose a neuromorphic analog design for continuous real-time learning. Our hardware design realizes the underlying principles of the neural engineering framework (NEF). NEF brings forth a theoretical framework for the representation and transformation of mathematical constructs with spiking neurons. Thus, providing efficient means for neuromorphic machine learning and the design of intricate dynamical systems. Our analog circuit design implements the neuromorphic prescribed error sensitivity (PES) learning rule with OZ neurons. OZ is an analog implementation of a spiking neuron, which was shown to have complete correspondence with NEF across firing rates, encoding vectors, and intercepts. We demonstrate PES-based neuromorphic representation of mathematical constructs with varying neuron configurations, the transformation of mathematical constructs, and the construction of a dynamical system with the design of an inducible leaky oscillator. We further designed a circuit emulator, allowing the evaluation of our electrical designs on a large scale. We used the circuit emulator in conjunction with a robot simulator to demonstrate adaptive learning-based control of a robotic arm with six degrees of freedom.

The positive drift at the end of nanoindentation P-h plot in case of hard titanium diboride (TiB2) films was ascribed to swelling taking place due to strain gradient plastic deformation with the sample contacting the indenter. The indentations performed at lower loads based on the positioning however suffered resistance towards any plastic deformation as per GTN theory. Although the formation of nanocrystallites inside larger grains were observed, indentation performed at grain boundaries or area having comparatively lower atomic density caused plastic deformation.

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Abstract content goes here Corresponding author(s) Email:   xxxxx@xxxx.xxx  

Recent advancements in deep learning have revolutionized the field of dental diagnostics, particularly in the detection and characterization of dental caries from radiographic images. Dental caries, a widespread oral health issue globally, necessitates precise and timely detection methods to mitigate its impact on patient health. Traditional diagnostic methods relying on visual inspection and radiographic imaging are subjective and prone to variability. In contrast, deep learning techniques offer a promising solution by automating detection processes through learned patterns from extensive datasets. This study proposes a comprehensive methodology utilizing state-of-the-art deep learning, specifically employing the YOLOv8.2.21 model for automatic detection and segmentation of dental caries from radiographs.