Results 231 to 240 of about 97,141 (337)

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Advanced Intelligent Systems, EarlyView.
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei‐Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C. L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano‐Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan, Tsz‐Kwan (Glory) Lee, Arbind Agrahari Baniya, Eisha Waseem, Abdul Rahman Sani, Abbas Z. Kouzani, Yijia Wu, Markus P. Nemitz, Masoud Shirzad, Dageon Oh, Seung Yun Nam, Amedeo Franco Bonatti, Irene Chiesa, Gabriele Maria Fortunato, Giovanni Vozzi, Carmelo De Maria, Mahdi Bodaghi   +37 more
wiley   +1 more source

Scalable Platform Enabling Reservoir Computing With Nanoporous Oxide Memristors for Image Recognition and Time Series Prediction

Advanced Intelligent Systems, EarlyView.
The approach of physical in materia computing incorporates parallel computing within the medium itself. A scalable and energy‐efficient, oxide‐based computational platform is realized in form of a nanoporous network of volatile niobium oxide memristors sandwiched between top and bottom metallic electrodes, and then tested for prediction and ...
Joshua Donald, Ben A. Johnson, Amir Mehrnejat, Alex Gabbitas, Arthur G. T. Coveney, Alexander G. Balanov, Sergey Savel’ev, Pavel Borisov   +7 more
wiley   +1 more source

From Mechanical Instability to Virtual Precision: Digital Twin Validation for Next-Generation MEMS-Based Eye-Tracking Systems. [PDF]

Sensors (Basel)
Pomianek M, Piszczek M, Stawarz P, Kucharczyk-Drab A.   +3 more
europepmc   +1 more source

Physical Reservoir Computing for Smart Manufacturing of PCB Using Crystallinity‐Controlled HfO2 Memristor for Reservoir

Advanced Intelligent Systems, EarlyView.
We address multilayer PCB anomaly detection for smart manufacturing by physical reservoir computing with HfO2‐based memristors. Crystallinity‐tuned HfO2 suppresses ferroelectricity while preserving the high‐k insulating state and confers strong short‐term memory.
Yongho Lee, Wonhee Lee, Junho Shin, Noah Jang, Hyunjun Kim, Da Ye Kim, Jinkyung Kim, June Soo Kim   +7 more
wiley   +1 more source

Can digital twin technology revolutionize public health emergency management? Insights from the PPRR framework. [PDF]

Front Public Health
Sun T, Lu J, Li Q, Kou Y.
europepmc   +1 more source

CAUIM: Amazonian Cassava‐Based Programmable Biocolloid

Advanced Intelligent Systems, EarlyView.
Bridging indigenous heritage and computation, this work introduces the usage of cauim, a traditional Amazonian cassava fermented beverage, as a programmable memristive‐like substrate and resistance switching. The material exhibits frequency‐dependent dynamics enabling reconfigurable Boolean logic operations.
Raphael Fortulan, Noushin Raeisi Kheirabadi, Clay Palmeira, Davin Browner, Alessandro Chiolerio, Andrew Adamatzky   +5 more
wiley   +1 more source

Digital neurosurgery in the era of intelligent medicine: a scoping review. [PDF]

Front Med (Lausanne)
Yuan Z, Shi Z, Wang Z.
europepmc   +1 more source

A digital twin for geophysical extremes: interim results from the DT-GEO project

The DT-GEO project (2022-2025), funded under the Horizon Europe topic call INFRA-2021-TECH-01-01, is implementing an interdisciplinary digital twin for modelling and simulating geophysical extremes at the service of research infrastructures and related communities.
openaire   +2 more sources

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source