Hierarchical malware detection, family identification, and variant attribution using CNN-based hybrid models on grayscale executable images. [PDF]
Saxena M, Das T.
europepmc +1 more source
Supporting AI Readiness Through Digital Workflows in Materials Science
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns +67 more
wiley +1 more source
Utilization of long-read sequencing for the detection of structural rearrangements with AgileStructure. [PDF]
Lascelles C +7 more
europepmc +1 more source
Near‐surface deuterium enrichment profiles for different microstructural types of TiAl after exposure at 700 °C in a heavy water‐containing environment. The deuterium levels are significantly higher than expected from natural occurrence, indicating that the heavy water dissociated during the exposure treatment and entered the specimens.
Jonathan D. H. Paul +5 more
wiley +1 more source
Occultation constraints on solar system formation models. [PDF]
Buie MW +3 more
europepmc +1 more source
A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin +14 more
wiley +1 more source
Differentially Expressed Gene Annotator (DEGAn): automated annotation and analysis of DEGs datasets with OS and PFS data. [PDF]
Agapito G, Cannataro M.
europepmc +1 more source
Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning
Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose +7 more
wiley +1 more source
On the Ellipticity of Close Binaries [PDF]
openaire +1 more source
Uncertainty Quantification with Domain Classification Models for Acetylcholinesterase Inhibition. [PDF]
Harris JS, Vignaux PA, Lane TR, Ekins S.
europepmc +1 more source

