Results 151 to 160 of about 1,588 (293)

Periodontitis severity and its social and clinical determinants: An ACES framework‐based NHANES analysis

Journal of Periodontology, EarlyView.
Abstract Objectives To assess associations between the poverty‐income ratio (PIR) and periodontitis severity using Application of the 2018 Periodontal Status Classification to Epidemiological Survey Data (ACES) framework. Methods Three NHANES cycles (2009–2014) with adults aged ≥30 years with complete periodontal examinations (n = 10,598) were included.
Meng Xuan Chen, Yue Yu, Chen Xuan Wei, Hamoun Sabri, Muhammad H. A. Saleh   +4 more
wiley   +1 more source

A Power‐Efficient Monte Carlo Framework for Nonlinear Light–Matter Interactions: In Silico Modeling of Spontaneous and Stimulated Raman Scattering in Turbid Media

Journal of Raman Spectroscopy, EarlyView.
Power‐efficient Monte Carlo modeling of nonlinear light–matter interactions in turbid media is demonstrated using Apple Silicon–accelerated photon transport. The Metal‐base framework enables accurate simulation of spontaneous and stimulated Raman scattering, revealing detection‐dependent SRS efficiency while providing a scalable, energy‐efficient ...
Ilya Vladyko, Vladislav Yakovlev, Alexander Doronin   +2 more
wiley   +1 more source

Separation of Time Scales in Weakly Interacting Diffusions. [PDF]

Arch Ration Mech Anal
Adams ZP, Engel M, Gvalani RS.
europepmc   +1 more source

Revealing the Resonant Physics of Open Photonic Time Crystals

Laser &Photonics Reviews, EarlyView.
ABSTRACT Photonic time crystals (PTCs) are media whose permittivity is modulated periodically in time, enabling momentum bandgaps and parametric amplification of light. Their realization at the nanoscale can revolutionize the study of light‐matter interactions.
Adrià Canós Valero, Sergei Gladyshev, David Globosits, Stefan Rotter, Egor A. Muljarov, Thomas Weiss   +5 more
wiley   +1 more source

Symbolic Calculus for a Class of Pseudodifferential Operators with Applications to Compactness. [PDF]

J Geom Anal
Bényi Á, Oh T, Torres RH.
europepmc   +1 more source

Predictive Modelling of Solvent Effects on Drug Incorporation into Polymeric Nanocarriers: A Machine Learning Approach

Macromolecular Rapid Communications, EarlyView.
When seeking nanoparticles with elevated drug loading content, the experimental setup, including solvent selection, is crucial. Through machine learning, we pinpointed that the drug's solubility in the organic solvent is the key factor for attaining high drug loading content.
Wei Ge, Ramindu De Silva, Yanan Fan, Scott A. Sisson, Martina H. Stenzel   +4 more
wiley   +1 more source

A numerical approach to fractional Volterra-Fredholm integro-differential problems using shifted Chebyshev spectral collocation. [PDF]

Sci Rep
Hamood MM, Sharif AA, Ghadle KP.
europepmc   +1 more source

Machine Learning Design of Tungsten Alloys With Strength–Ductility Synergy

Materials Genome Engineering Advances, EarlyView.
This study integrates machine learning with solid solution softening knowledge to design multielement tungsten alloys with strength‐ductility synergy. ML models identify promising compositions within W‐ Ta ‐ Re system that exhibit pronounced chemical short‐range order, resulting in exceptional mechanical performance. Experimental validation through the
Juan Ding, Jiayi Liu, Jiatao Zhou, Baishan Chen, Yunzhu Ma, Wensheng Liu, Yufeng Huang, Chaoping Liang   +7 more
wiley   +1 more source

Bifurcation analysis and analytical traveling wave solutions of a sasa-satsuma equation involving beta, M-truncated and conformable derivatives using the EGREM method. [PDF]

Sci Rep
Munir F, Saad KM, Abbas M, Birhanu A, Hamanah WM, Nazir T.   +5 more
europepmc   +1 more source

CONTINUOUS AND DISCRETE TIME WAVEFORM RELAXATION METHODS FOR {V}OLTERRA INTEGRAL EQUATIONS WITH WEAKLY SINGULAR KERNELS

, 2002
The authors present a waveform relaxation (WR)method for systems of nonlinear Volterra integral equations (of the second kind) with a weakly singular kernel. A convergence analysis is performed for the continuous and discrete WR methods.
RUSSO, ELVIRA, M. CRISCI, BRUNNER H., VECCHIO A.   +3 more
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