Results 201 to 210 of about 44,999 (296)

Machine Learning‐Driven Digital Twin of a Field‐Effect Transistor‐Based Hormone Biosensor for Real‐Time Estradiol Monitoring

Advanced Intelligent Systems, EarlyView.
A machine learning‐driven digital twin simulates an aptamer‐functionalized BioFET measuring 17β‐estradiol. Real‐time Isd signals are processed, features are extracted, and trained models estimate hormone concentration. In parallel, a one‐step‐ahead forward model learns biosensor dynamics and generates realistic synthetic signals, enabling in silico ...
Anastasiia Gorelova, Alexandra Parichenko, Shirong Huang, Santiago Melia, Gianaurelio Cuniberti   +4 more
wiley   +1 more source

A Data Rate Monitoring Approach for Cyberattack Detection in Digital Twin Communication. [PDF]

Sensors (Basel)
Rodrigues C, Júnior WSS, Oliveira W, Lima I.   +3 more
europepmc   +1 more source

Wireless, Battery‐Free Wearable Pressure Sensor for Compression Therapy Entirely Built on Surface Mount Devices

Advanced Intelligent Systems, EarlyView.
This work presents a flexible, battery‐free sensor that forms a closed‐loop feedback system for smart compression therapy devices. Its scalable surface‐mount manufacturing, compact form factor, and wireless communication enable seamless integration with a wide range of active compression garments, improving user experience and therapeutic outcomes ...
Sinuo Zhao, Hongru Chen, Isaac Seeto, Phuoc Thien Phan, Chi Cong Nguyen, Tran Bach Dang, Kefan Zhu, Michael Listyawan, Minh Cuong Duong, Alice Ma, Thanh Nho Do, Hoang‐Phuong Phan   +11 more
wiley   +1 more source

From images to physics-based computational models to digital twins: a framework for personalized cancer therapies. [PDF]

Front Radiol
Moradi Kashkooli F, Zhan W, Bhandari A, Yusufaly TI, Kolios MC, Rahmim A, Soltani M.   +6 more
europepmc   +1 more source

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

Memetic/Metaphorical Digital Twins: Extending Knowledge Co-Creation Across Economics, Architecture, and Beyond. [PDF]

Biomimetics (Basel)
Schmitt U.
europepmc   +1 more source

A Flexible and Energy‐Efficient Compute‐in‐Memory Accelerator for Kolmogorov–Arnold Networks

Advanced Intelligent Systems, EarlyView.
This article presents KA‐CIM, a compute‐in‐memory accelerator for Kolmogorov–Arnold Networks (KANs). It enables flexible and efficient computation of arbitrary nonlinear functions through cross‐layer co‐optimization from algorithm to device. KA‐CIM surpasses CPU, ASIC, VMM‐CIM, and prior KAN accelerators by 1–3 orders of magnitude in energy‐delay ...
Chirag Sudarshan, Oliver Artner, Paul‐Philipp Manea, Sebastian Siegel, Susanne Hoffmann‐Eifert, Regina Dittmann, John Paul Strachan   +6 more
wiley   +1 more source

Multi-scale digital twins for personalized medicine. [PDF]

Front Digit Health
Vallée A.
europepmc   +1 more source

Controlling Dynamical Systems Into Unseen Target States Using Machine Learning

Advanced Intelligent Systems, EarlyView.
Parameter‐aware next‐generation reservoir computing enables efficient, data‐driven control of dynamical systems across unseen target states and nonstationary transitions. The approach suppresses transient behavior while navigating system collapse scenarios with minimal training data—over an order of magnitude less than traditional methods.
Daniel Köglmayr, Alexander Haluszczynski, Christoph Räth   +2 more
wiley   +1 more source