Towards cross-domain few-shot modulation classification: a feature transformation graph neural network approach. [PDF]
Shi Y, Xu H, Qi Z, Wang D, Meng Q.
europepmc +1 more source
Graph Signal Processing: Reconstruction Algorithms
In this work, we study the common features and properties of signals defined on graphs and we focus on a specific application related to the reconstruction of graph signals in both centralized and distributed ...
openaire +1 more source
This study examines how pore shape and manufacturing‐induced deviations affect the mechanical properties of 3D‐printed lattice materials with constant porosity. Combining µ‐CT analysis, FEM, and compression testing, the authors show that structural imperfections reduce stiffness and strength, while bulk material inhomogeneities probably enhance ...
Oliver Walker +5 more
wiley +1 more source
Implementation of Drug‐Induced Rhabdomyolysis and Acute Kidney Injury in Microphysiological System
A modular Muscle–Kidney proximal tubule‐on‐a‐chip integrates 3D skeletal muscle and renal proximal tubule tissues to model drug‐induced rhabdomyolysis and acute kidney injury. The coculture system enables dynamic tissue interaction, functional contraction monitoring, and quantification of nephrotoxicity, revealing drug side effect‐induced metabolic ...
Jaesang Kim +4 more
wiley +1 more source
Fault Diagnosis Method for Reciprocating Compressors Based on Spatio-Temporal Feature Fusion. [PDF]
Xu H +6 more
europepmc +1 more source
MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
We introduce a nucleic acid nanoparticle (NANP) platform designed to be rrecognized by the human innate immune system in a regulated manner. By changing chemical composition while maintaining constant architectural parameters, we identify key determinants of immunorecognition enabling the rational design of NANPs with tunable immune activation profiles
Martin Panigaj +21 more
wiley +1 more source
Structure-Function Dynamics in Healthy Cognitive Aging: A Graph Signal Processing Approach
Guichet C +3 more
europepmc +1 more source
Graph Convolutional Networks Enable Fast Hemorrhagic Stroke Monitoring With Electrical Impedance Tomography. [PDF]
Toivanen J +5 more
europepmc +1 more source

