Results 211 to 220 of about 5,042 (302)

Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

Advanced Intelligent Discovery, EarlyView.
Multilayer acoustic superscatterers are designed using machine learning to achieve broadband superscattering and strong sound insulation. By incorporating a weighted mean absolute error into the loss function, the forward and inverse neural networks accurately map structural parameters to spectral responses.
Lijuan Fan, Xiangliang Zhang, Ying Wu
wiley   +1 more source

Upconversion optical entropy encoding for infrared complex-amplitude imaging. [PDF]

Light Sci Appl
Zhu SK, Pan T, Tang CX, Li AH, Zheng ZH, Xu Y, Li X, Chen JH.   +7 more
europepmc   +1 more source

Holographic Mapping of Orbital Angular Momentum using a Terahertz Diffractive Optical Neural Network

Advanced Intelligent Discovery, EarlyView.
A compact six‐layer diffractive optical neural network enables direct recognition and spatial mapping of terahertz (THz) orbital angular momentum (OAM) beams. Fabricated by 3D printing, the system distinguishes nine OAM modes and their superpositions with high fidelity, good efficiency, and low crosstalk, offering a scalable solution for THz ...
Wei Jia, Miguel Gomez, Steve Blair, Berardi Sensale‐Rodriguez   +3 more
wiley   +1 more source

Analysis of Heat-Mass Transfer and Exergy in Heating Process of Chilled Ready-to-Eat Foods Using High-Power Microwave Ovens. [PDF]

Int J Food Sci
Klinbun J, Rattanadecho P, Klinbun W.
europepmc   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Roadmap towards personalized approaches and safety considerations in non-ionizing radiation: from dosimetry to therapeutic and diagnostic applications. [PDF]

Phys Med Biol
Laakso I, Paulides MM, Kodera S, Ahn S, Brace CL, Cavagnaro M, Chen J, Deng ZD, De Santis V, Diao Y, Farrugia L, Feliziani M, Fiocchi S, Fioranelli F, Hikage T, Makaroff S, Mizuno M, Opitz A, Pickwell-MacPherson E, Prakash P, Rodrigues DB, Sasaki K, Sakamoto T, Taylor Z, Visser HJ, Yeo DTB, Hirata A.   +26 more
europepmc   +1 more source

Fluorescent Hydrogel‐Based Strain Sensor With Machine Learning‐Augmented Performance

Advanced Intelligent Discovery, EarlyView.
Fluorescent hydrogel strain sensor based on carbon quantum dots enabling optical readout of deformation through strain‐dependent emission changes, coupled with Random Forest analysis to capture nonlinear fluorescence‐concentration relationships and identify optimal sensing conditions. Hydrogels are ideal matrices for bio‐integrated wearable sensors due
Tailai Chen, Tianlong Liu, Chao Lu, Rimsha Malik, Jin Zhang   +4 more
wiley   +1 more source

A Novel Denoising Method for Mud Continuous-Wave Signals Based on Selective Ensemble Strategy with Particle Swarm Optimization. [PDF]

Sensors (Basel)
Huang C, Cai W, Pang D, Yang Y, Du X, Duan X, Zhou Y.   +6 more
europepmc   +1 more source

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker, Tamzid Ul Islam, Md. Robiul Islam   +2 more
wiley   +1 more source

Learning stable radiation boundaries for wave simulations via passive neural state-space models. [PDF]

iScience
Li A, Wang X, Wang Y, Wang S.
europepmc   +1 more source