Results 151 to 160 of about 39,218 (293)
Adaptive Twisting Metamaterials
This work introduces torque‐controlled twisting metamaterials as a transformative platform for adaptive crashworthiness. By combining multiscale predictive modeling with experimental validation on additively manufactured gyroids, it demonstrates tunable stiffness, collapse stress, and energy absorption.
Mattia Utzeri +6 more
wiley +1 more source
Lightweight authentication for IoT devices (LAID) in sustainable smart cities. [PDF]
Khalique A +3 more
europepmc +1 more source
Working Out the New Algorithm Enciphered the Data with a Symmetric Key
Shukhratjon Umarov +1 more
openalex +1 more source
This work proposes neuromorphic visual receptive field hardware with vertically integrated amorphous In‐Ga‐Zn‐O optoelectronic memristors and Si neuron transistors for retina‐inspired visual processing. The visual receptive field array, comprising ON‐ and OFF‐type cells, facilitates edge detection, thereby enhancing perception in complex images, such ...
Hyun Wook Kim +7 more
wiley +1 more source
A novel memristor-based hyperchaotic hybrid encryption system with DNA for image encryption on the Jetson TX2. [PDF]
Ulutas H.
europepmc +1 more source
NEW ALGORITHM OF BLOCK ENCRYPTION OF DATA WITH THE SYMMETRIC KEY
Д. Е. Акбаров +1 more
openalex +2 more sources
Manganese‐Based Spinel Cathodes: A Promising Frontier for Solid‐State Lithium‐Ion Batteries
Mn‐based spinel cathodes LiMn2O4 (LMO) and LiNi0.5Mn1.5O4 (LNMO), with the unique characteristics of low cost, structural stability, and 3D Li‐ion diffusion channels, have presented great potential in all‐solid‐state batteries. Here, a comprehensive understanding and valuable insights into the rational design and implications for the future development
Yu Dou +6 more
wiley +1 more source
A Blockchain-Enabled Multi-Authority Secure IoT Data-Sharing Scheme with Attribute-Based Searchable Encryption for Intelligent Systems. [PDF]
Zhang F, Xia X, Gao H, Ma Z, Chen X.
europepmc +1 more source
Learning Crystallographic Disorder: Bridging Prediction and Experiment in Materials Discovery
Machine learning based computational materials discovery workflows have recently proposed thousands of potentially stable crystalline materials. However, the experimental realization of these predictions is often challenging because the models assume perfectly ordered structures.
Konstantin S. Jakob +3 more
wiley +1 more source

