Results 201 to 210 of about 739,829 (293)

Categorization of echocardiograms by humans and pigeons. [PDF]

Front Psychol
Orr ORP, Navarro VM, Wasserman EA, Ouyang D.   +3 more
europepmc   +1 more source

The Synergism of β‐Cyclodextrin and Fe3+ Enabled Anti‐Swelling Ion‐Conductive Hydrogels for Multimodal Underwater Sensing Aided by Machine Learning Algorithms

Advanced Science, EarlyView.
An anti‐swelling ion‐conductive hydrogel is developed via β‐cyclodextrin and Fe3+ synergism, enabling reliable underwater motion sensing and Morse code communication. Integrated with machine learning, the system achieves 98.6% accuracy in real‐time underwater handwriting recognition for rapid information transmission.
Hao Dong, Chuanliu Liu, Yiming Luo, Dongyu Si, Menghan Zhao, Fangyu Zhao, Yixue Zhang, Wei Zeng, Eman Ramadan Elsharkawy, Terence Xiaoteng Liu, Zeinhom M. El‐Bahy, Hua Hou, Zhanhu Guo, Huige Wei   +13 more
wiley   +1 more source

Artificial Intelligence Generated Videos as Supportive Tools in Medical Education: A Scoping Review. [PDF]

J Med Educ Curric Dev
Impito PF.
europepmc   +1 more source

Ethical and Frugal Approaches to Animal Experimentation in Bioelectronics and Neural Engineering—An Invertebrate Renaissance?

Advanced Electronic Materials, EarlyView.
Invertebrates are the classic neuroscience models and should make a comeback. Invertebrate organisms can be a more ethical and cost‐effective way to move bioelectronics research forward more rapidly. ABSTRACT The accelerating development of bioelectronic neural interfaces has brought increased attention to ethical considerations surrounding in vivo ...
Eric Daniel Głowacki
wiley   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

OpenAI's Sora in medical education: artificial videos in the classroom of the future Sora d'OpenAI dans l'enseignement médical : des vidéos artificielles dans la salle de classe du futur. [PDF]

Can Med Educ J
Waisberg E, Ong J, Kumar R, Masalkhi M, Lee AG.   +4 more
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

MSRCTNet: a novel multi-scale capsule triplet network for efficient redundant frame removal in wireless capsule endoscopy videos. [PDF]

Sci Rep
Li Q, Wang S, Cheng Z, Zhang Q, Li J, Tu J.   +5 more
europepmc   +1 more source