Results 151 to 160 of about 594,620 (381)
Background: Smile is one of the most important factors determining the attractiveness of a person and has an important role in his mood and influence on others.
B. Ranjbar Omidi +4 more
doaj
ARE THE GINGIVAL DISPLAY AND THE SMILE ARC IN MALES AND FEMALES DIFFERENT?
Highlights • Gingival display and smile arc enhance the aesthetic value of a person's smile and may be influenced through dental treatment. • The individual profile photographs taken during social smiles can be used to evaluate dental treatment plans.
Wita Anggraini +3 more
doaj +1 more source
Smiles as elements of virtual communication (Смайли як елементи віртуального спілкування) [PDF]
This article presents the definition of the word «Smile», analyses the peculiarities of applying the term «smile», the history of its origin and its role in the process of virtual communication.
Жуков , В. ( W. Zukow ) +1 more
core
Meaning in Life of Terminally Ill Parent with Young Children – A Quantitative Evaluation with SMiLE [PDF]
Henning Cuhls +4 more
openalex +1 more source
K L, Stevenson, W C, Welch
openaire +2 more sources
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng +4 more
wiley +1 more source
Cross-currency smile calibration [PDF]
We document the numerical aspects of the calibration of cross-currency options on the local volatility framework. We consider the partial differential equation satisfied by the price of the cross-currency option and see that the most important ...
Gabriel Turinici, Marc Laillat
core
Analysis of Smile Perception in Laypersons and Orthodontists: A Cross Sectional Comparative Study
Harshit Naik +2 more
openalex +1 more source
A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley +1 more source

