Results 11 to 20 of about 42,522 (193)
A stress‐normalised sensitivity metric (S = G/Y) is introduced as a materials‐level benchmark for intrinsically piezoresistive nanocomposites. By decoupling electromechanical response (G) from stiffness (Y), the framework enables direct comparison across diverse systems and clarifies design trade‐offs for wearable sensors.
Conor S. Boland
wiley +1 more source
An efficient deep learning model for brain tumour detection with privacy preservation
Abstract Internet of medical things (IoMT) is becoming more prevalent in healthcare applications as a result of current AI advancements, helping to improve our quality of life and ensure a sustainable health system. IoMT systems with cutting‐edge scientific capabilities are capable of detecting, transmitting, learning and reasoning.
Mujeeb Ur Rehman +8 more
wiley +1 more source
Spatial‐Wavelength Multiplexing Error‐Controlled Photonic Analog Computing System
A novel photonic integrated circuit prototype implementing the concept of general‐purpose analog computing and demonstrate its capability in radio frequency applications. The chip features a multichannel architecture and performs fully optical analog computation with frequency‐domain parallel processing. An FPGA‐based error‐correction algorithm aims to
Tao Zhu +15 more
wiley +1 more source
A Perspective on Interactive Theorem Provers in Physics
Into an interactive theorem provers (ITPs), one can write mathematical definitions, theorems and proofs, and the correctness of those results is automatically checked. This perspective goes over the best usage of ITPs within physics and motivates the open‐source community run project PhysLean, the aim of which is to be a library for digitalized physics
Joseph Tooby‐Smith
wiley +1 more source
Physical Origin of Temperature Induced Activation Energy Switching in Electrically Conductive Cement
The temperature‐induced Arrhenius activation energy switching phenomenon of electrical conduction in electrically conductive cement originates from structural degradation within the biphasic ionic‐electronic conduction architecture and shows percolation‐governed characteristics: pore network opening dominates the low‐percolation regime with downward ...
Jiacheng Zhang +7 more
wiley +1 more source
Synchronization of Analog Neuron Circuits With Digital Memristive Synapses: An Hybrid Approach
An hybrid circuit mimicking neural units coupled using memristive synapses is introduced. The analog neurons provide flexibility and robustness, and the digital memristive coupling guarantees the full reconfigurability of the interconnection. The onset of a synchronized spiking behavior in two circuits mimicking the Izhikevich neuron is discussed from ...
Lamberto Carnazza +3 more
wiley +1 more source
AI in chemical engineering: From promise to practice
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
Abstract Electrification of distillation offers a promising route to reducing scope‐1 emissions from one of the chemical industry's most energy‐intensive unit operations. However, conventional adiabatic columns are dynamically inflexible: Long, energy‐intensive start‐ups make shutdown and restart impractical under variable electricity prices and ...
Samuel Mercer, Michael Baldea
wiley +1 more source
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley +1 more source
This study proposes a deep learning approach to evaluate the fatigue crack behavior in metals under overload conditions. Using digital image correlation to capture the strain near crack tips, convolutional neural networks classify crack states as normal, overload, or recovery, and accurately predict fatigue parameters.
Seon Du Choi +5 more
wiley +1 more source

