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CD-PHY: Physical layer security in wireless networks through constellation diversity [PDF]
MILCOM 2012 - 2012 IEEE Military Communications Conference, 2012 A common approach for introducing security at the physical layer is to rely on the channel variations of the wireless environment. This type of approach is not always suitable for wireless networks where the channel remains static for most of the network lifetime.
Mohammad Iftekhar Husain +2 more
openaire +2 more sources
Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks
Advanced Science, EarlyView.A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu +5 more
wiley +1 more source
Advanced Science, EarlyView.Magnetomechanical neuromodulation using magnetic nanodiscs enables remote activation of neurons. In a hemiparkinsonian mouse model, alternating magnetic fields actuate the nanodiscs to generate torque that opens mechanosensitive ion channels within the subthalamic nucleus, thereby modulating basal ganglia motor circuitry.
Anouk Wolters +12 more
wiley +1 more source
Advanced Science, EarlyView.A novel PR strategy was proposed to improve CBD‐derived Sb2S3 film quality. It synergistically optimized the CBO of CdS/Sb2S3 interface and the back‐contact barrier, while the VS2 donor defect was converted into SSb2 acceptor defect with lower concentration.
Yeyang Lin +8 more
wiley +1 more source
Physics‐encoded transfer learning for scale‐up modeling of CHO cell bioreactors
AIChE Journal, EarlyView.Abstract Developing reliable predictive models for mammalian cell bioreactors, particularly Chinese hamster ovary (CHO) cultures widely used in biopharmaceutical manufacturing, remains challenging due to severe data scarcity in industrial‐scale reactors.
Muyang Li, Ming Xiao, Zhe Wu
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee +3 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng +4 more
wiley +1 more source