Results 221 to 230 of about 91,388 (321)

Spherical Harmonics for the 1D Radiative Transfer Equation. II. Thermal Emission [PDF]

Caoimhe M. Rooney, Natasha E. Batalha, Mark S. Marley   +2 more
openalex   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Head-Specific Spatial Spectra of Electroencephalography Explained: A Sphara and BEM Investigation. [PDF]

Biosensors (Basel)
Graichen U, Klee S, Fiedler P, Hofmann L, Haueisen J.   +4 more
europepmc   +1 more source

The Spherical Harmonic Model of Gravity Field in Mainland China by Slepian Local Spectrum Method

, 2017
Shi Chen, XU Wei-min, Qianshen Wang
openalex   +2 more sources

3D investigation and modeling of the geometric effects on porosity in packed beds

AIChE Journal, EarlyView.
Abstract In porous beds, physical boundaries restrict particle arrangement, leading to inhomogeneous porosity. This paper reports on the porosity profiles that are the result of geometric effects on monodisperse packed beds in cylindrical and cubic arrangements. Special focus is given to the influence of edges and corners in cubic geometries.
Bastian Oldach, Konrad E. R. Boettcher, Alexander S. Sommer‐Behr, Norbert Kockmann   +3 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Expansion of 3D vector fields and second-rank tensor fields in spherical harmonics (SHE). Notes from 1999 by Charles Zemach [Retired, LANL Scientist and Affiliate (1976-2004)]. Submitted by S. Kurien on behalf of C. Zemach.

, 2016
Charles Zemach, Susan Kurien
openalex   +2 more sources

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Inverse-designed gyrotropic scatterers for non-reciprocal analog computing. [PDF]

Nanophotonics
Hadjiantoni N, Goh H, Hanham SM, Navarro-Cía M, Alù A.   +4 more
europepmc   +1 more source

High-performance fluorescence molecular tomography through shape-based reconstruction using spherical harmonics parameterization. [PDF]

PLoS One, 2014
Wang D, He J, Qiao H, Song X, Fan Y, Li D.   +5 more
europepmc   +1 more source