Results 121 to 130 of about 496,088 (288)

A proposed architectural learner model for a personalized learning environment. [PDF]

Educ Inf Technol (Dordr), 2023
Bellarhmouch Y, Jeghal A, Tairi H, Benjelloun N.   +3 more
europepmc   +1 more source

Chemically Doped Conductive Polymers for Wearable Health Monitoring

Advanced Materials Technologies, EarlyView.
Among conductive polymers, poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), polyaniline (PANI), and polypyrrole (PPy) are the most studied and applied. Chemical doping significantly boosts intrinsic conductivity and mechanical robustness.
Mengdi Zuo, Jian Song, Hong Hu, Le Zheng, Lei Zhang, Charles H. Lawrie   +5 more
wiley   +1 more source

An efficient personalized federated learning approach in heterogeneous environments: a reinforcement learning perspective

Scientific Reports
In order to address the problem of data heterogeneity, in recent years, personalized federated learning has tailored models to individual user data to enhance model performance on clients with diverse data distributions.
Hongwei Yang, Juncheng Li, Meng Hao, Weizhe Zhang, Hui He, Arun Kumar Sangaiah   +5 more
doaj   +1 more source

The Charm or Chasm of Digital Personalized Learning in Education: Teachers' Reported Use, Perceptions and Expectations. [PDF]

TechTrends, 2023
Van Schoors R, Elen J, Raes A, Vanbecelaere S, Depaepe F.   +4 more
europepmc   +1 more source

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

Analysis and Construction of the User Characteristic Model in the Adaptive Learning System for Personalized Learning. [PDF]

Comput Intell Neurosci, 2022
Zhao X, Long S, Hu D.
europepmc   +1 more source

Smart Closed‐Loop Systems in Personalized Healthcare: Advances and Outlook

Advanced Materials Technologies, EarlyView.
A smart closed‐loop e‐textile integrates multimodal sensing, onboard processing, wireless communication, and wearable power to enable real‐time physiological/biochemical monitoring and feedback‐controlled therapy. ABSTRACT Smart textiles represent a revolutionary frontier in healthcare, seamlessly blending fabric and advanced technologies to create ...
Safoora Khosravi, Sang Won Lee, Mohammad Abrar Uddin, Sina Hassanpoor, Neha Suvindran, Katherine Le, Harun Arkaz, Harishkumar Narayana, Saeid Soltanian, Danial Khorsandi, Negar Hosseinzadeh Kouchehbaghi, Taeil Kim, Yangzhi Zhu   +12 more
wiley   +1 more source

Transparency in Admissions and Personalized Learning Through Resident Patient Selection. [PDF]

Ochsner J, 2022
Archibald A, Zimmerman P, Seay W, Verma L, Wilson J, Sharma P.   +5 more
europepmc   +1 more source

Fabric‐Based Wearable Robotic Exoskeleton Gloves: Advancements and Challenges

Advanced Materials Technologies, EarlyView.
This review highlights interdisciplinary technological advances in fabric‐based robotic gloves, focusing on progress in design, fabrication, actuation, sensing, control, and power and energy requirements. It also addresses performance testing and validation, including biomechanical, strength, functional, user experience, and durability assessments, to ...
Ayse Feyza Yilmaz, Gökhan Ince, Ozgur Atalay   +2 more
wiley   +1 more source

Who's learning? Using within-family studies to understand personalized learning. [PDF]

NPJ Sci Learn, 2021
von Stumm S, Wertz J.
europepmc   +1 more source