All-polymer piezo-ionic-electric electronics.

Piezoelectric electronics possess great potential in flexible sensing and energy harvesting applications. However, they suffer from low electromechanical performance in all-organic piezoelectric systems due to the disordered and weakly-polarized interfaces. Here, we demonstrated an all-polymer piezo-ionic-electric electronics with PVDF/Nafion/PVDF (polyvinylidene difluoride) sandwich structure and regularized ion-electron interfaces.

Novel embedding model predicting the credit card's default using neural network optimized by harmony search algorithm and vortex search algorithm.

In today's banking and financial system, using a credit card has become indispensable. The credit card industry has existed due to a shift in consumer preferences and a rise in national economic growth. The number of issuing banks, card issuers, and transaction volumes has increased significantly.

Mass-Produced Skin-Inspired Piezoresistive Sensing Array with Interlocking Interface for Object Recognition.

E-skins, capable of responding to mechanical stimuli, hold significant potential in the field of robot haptics. However, it is a challenge to obtain e-skins with both high sensitivity and mechanical stability. Here, we present a bioinspired piezoresistive sensor with hierarchical structures based on polyaniline/polystyrene core-shell nanoparticles polymerized on air-laid paper.

Hierarchical Piezoelectric Composites for Noninvasive Continuous Cardiovascular Monitoring.

Continuous monitoring of blood pressure (BP) and multiparametric analysis of cardiac functions are crucial for the early diagnosis and therapy of cardiovascular diseases. However, existing monitoring approaches often suffer from bulky and intrusive apparatus, cumbersome testing procedures, and challenging data processing, hampering their applications in continuous monitoring. Here, a heterogeneously hierarchical piezoelectric composite is introduced for wearable continuous BP and cardiac function monitoring, overcoming the rigidity of ceramic and the insensitivity of polymer.

Two-Stream Mixed Convolutional Neural Network for American Sign Language Recognition.

The Convolutional Neural Network (CNN) has demonstrated excellent performance in image recognition and has brought new opportunities for sign language recognition. However, the features undergo many nonlinear transformations while performing the convolutional operation and the traditional CNN models are insufficient in dealing with the correlation between images. In American Sign Language (ASL) recognition, J and Z with moving gestures bring recognition challenges.