Results 61 to 70 of about 64,340 (254)
Coarse‐grained (left) and atomistic (right) models of the shape memory polymer ESTANE ETE 75DT3 are shown schematically. The two representations bridge molecular detail and mesoscopic description. Both models capture shape memory behavior, linking segmental mobility and conformational relaxation of anisotropic chains to macroscopic recovery, and ...
Fathollah Varnik
wiley +1 more source
GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models
Methods for clustering in unsupervised learning are an important part of the statistical toolbox in numerous scientific disciplines. Tewari, Giering, and Raghunathan (2011) proposed to use so-called Gaussian mixture copula models (GMCM) for general ...
Anders Ellern Bilgrau +5 more
doaj +1 more source
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran +6 more
wiley +1 more source
Grain boundary triple junctions are an essential ingredient of the microstructure of polycrystalline materials. In this study, a triple junction is observed using atomic‐resolution scanning transmission electron microscopy and characterized. Computer simulations reveal that the junction has a dislocation character that is determined by the joining ...
Tobias Brink +4 more
wiley +1 more source
A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle +5 more
wiley +1 more source
Product of Gaussian Mixture Diffusion Models
AbstractIn this work, we tackle the problem of estimating the density$$ f_X $$fXof a random variable$$ X $$Xby successive smoothing, such that the smoothed random variable$$ Y $$Yfulfills the diffusion partial differential equation$$ (\partial _t - \Delta _1)f_Y(\,\cdot \,, t) = 0 $$(∂t-Δ1)fY(·,t)=0with initial condition$$ f_Y(\,\cdot \,, 0) = f_X $$fY(
Zach, Martin +3 more
openaire +3 more sources
Experiments and thermophysical simulations were conducted to investigate the electron beam powder bed fusion electron beam (PBF‐EB/M) process for the γ′‐strengthened nickel‐based superalloy Inconel 738LC. The results demonstrate the impact of process‐induced microstructural variations on high‐temperature mechanical behavior, providing a basis for ...
Jan Niklas Petenati +11 more
wiley +1 more source
Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models
Personal wearables such as smartphones or smartwatches are increasingly utilized in everyday life. Frequently, activity recognition is performed on these devices to estimate the current user status and trigger automated actions according to the user ...
Martin Jänicke +2 more
doaj +1 more source
Deep‐UV (258 nm) femtosecond pulses enable uniform amorphous silicon writing on Si(100)/(111) with a six‐fold larger fluence amorphization window than NIR methods. Optimized fluence and overlap yield 20–45 nm uniform and continuous amorphous layers. Microscopy shows sharp interfaces, and real‐time reflectivity reveals nanosecond melt–resolidification ...
Wissal Benali +5 more
wiley +1 more source
Solvate ionic liquids lubrication reduced the coefficient of friction by ∼60% compared to dry sliding, reaching steady‐state values as low as 0.04–0.05. Corrosion weight‐loss measurements in 1 M HCl further demonstrated significant inhibition behavior, with only 100 ppm of [Li(G3)][TFSI] (∼68.5 μL/L) reducing corrosion‐product weight loss by 63 ...
Sameh Dabees +6 more
wiley +1 more source

