Results 211 to 220 of about 148,378 (289)

Biomimetic Multiscale‐Structured Biomass Graphene/Polyurethane Sponge Composite for Flexible Pressure Sensors and Intelligent Cushioning Materials

Advanced Science, EarlyView.
In this study, a multifunctional conductive sponge composite (MAPU) is developed using a biomimetic, hierarchical construction strategy emulating the growth process of natural wood. This approach involved integrating biomass‐derived graphitic nanoflakes, synthesized from luffa vine (LVGN), into a polyurethane (PU) sponge matrix.
Runmin Xu, Pusen Cao, Tingting Zhang, Jie Wei, Yue Wang, Zhi Huang, Zhenghang Chen, Jianfeng Xi, Yong Guo, Yuxia Chen   +9 more
wiley   +1 more source

A New Perspective in Epileptic Seizure Classification: Applying the Taxonomy of Seizure Dynamotypes to Noninvasive EEG and Examining Dynamical Changes across Sleep Stages. [PDF]

eNeuro
Guendelman M, Vekslar R, Shriki O.
europepmc   +1 more source

PREVALENCE OF DEPRESSION AND ITS IMPACT ON QUALITY OF SLEEP AMONG VARIOUS STAGES OF KNEE OSTEOARTHRITIS

Muhammad Abrar, Syed Makhdoom Hussain, Intsam Aslam, Muhammad Sohail Abbas, Sehrish Khan, Ramsha Kanwal   +5 more
openalex   +1 more source

H⁺ Exchange‐Driven ppb‐Level and High‐Selective Formaldehyde Detection at Room Temperature for Environmental and Clinical Applications

Advanced Science, EarlyView.
This article presents an innovative room‐temperature formaldehyde sensor based on H⁺ exchange method, achieving ppb‐level detection limit and exceptional selectivity. The study highlights its practical potential for real‐time environmental monitoring and clinical diagnostic applications.
Lubing Cai, Mengyang Pang, Zhaosong Liu, Yanfei Li, Jiani Li, Zhaorui Zhang, Fengshuang Zheng, Chao Li, Ang Zheng, Xuemin Zhang   +9 more
wiley   +1 more source

SlumberNet: deep learning classification of sleep stages using residual neural networks. [PDF]

Sci Rep
Jha PK, Valekunja UK, Reddy AB.
europepmc   +1 more source

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

J Med Internet Res, 2023
Tran HH, Hong JK, Jang H, Jung J, Kim J, Hong J, Lee M, Kim JW, Kushida CA, Lee D, Kim D, Yoon IY.   +11 more
europepmc   +1 more source

Artificial Intelligence‐Driven Soft Bioelectronics for Self‐Powered Respiration Monitoring

Advanced Science, EarlyView.
Artificial intelligence‐driven soft bioelectronics for self powered respiration monitoring based on triboelectric nanogenerators (TENGs), piezoelectric nanogenerators (PENGs), and magnetoelastic generators (MEGs) enable continuous and multi‐scenario respiratory biomechanical data collection. Coupled with machine learning and big data driven diagnostics,
Xinkai Xu, Xiao Xiao, Rui Guo, Jun Chen
wiley   +1 more source

Classification and automatic scoring of arousal intensity during sleep stages using machine learning. [PDF]

Sci Rep
Han H, Seong MJ, Hyeon J, Joo E, Oh J.
europepmc   +1 more source

Empowering Flexible Electronics with Piezoelectric Nanogenerators: Breakthroughs from Energy Harvesting to Intelligent Sensing

Advanced Science, EarlyView.
This review systematically summarizes recent advances in self‐powered flexible sensing and energy storage systems based on piezoelectric nanogenerators (PENGs). It highlights their fundamental mechanisms, structural designs, and applications in health monitoring, e‐skin, and tactile sensing. The integration of PENGs with supercapacitors or batteries is
Wu‐Lin Xin, He‐Qing Cai, Xi Cui, Lu Han, Kou Zhang, Xin‐Yu Xue, Yi‐Fei Song, Juan Liu, Zhou Li   +8 more
wiley   +1 more source

Automatic classification of sleep stages using EEG signals and convolutional neural networks. [PDF]

PLoS One
Masad IS, Alqudah A, Qazan S.
europepmc   +1 more source