The purpose of this study was to evaluate the caries experience among 6-12-year-old indigenous (Naporunas) and non-indigenous (recent settlers of mixed ethnic origin) schoolchildren, living in the Amazon basin of Ecuador. Cross-sectional data were obtained from 1,449 clinical exams according to the World Health Organization criteria. Nine (7.6%) indigenous and 3 (4.5%) non-indigenous children had no caries experience in their primary dentition at the age of 6. The mean dmft value (SD) among indigenous and non-indigenous children aged 6 was 6.40 (3.36) and 8.36 (3.93), respectively. Sixty-four (54.2%) indigenous and 29 (43.3%) non-indigenous children had no caries experience in their permanent first molars at the age of 6. Only 7 (6.26%) indigenous and 2 (2.60%) non-indigenous children were caries-free at the age of 12. The mean DMFT values (SD) for 12-year-olds were 4.47 (2.85) among indigenous and 5.25 (2.89) among non-indigenous children. Fillings were almost non existent. Caries rates were high among both groups, with untreated carious lesions predominating in all ages. The data of indigenous children suggest adoption of a non-traditional diet. An appropriate oral health response based primarily on prevention and health promotion is needed.
Ion temperatures of over 100 million degrees Kelvin (8.6keV) have been produced in the ST40 compact high-field spherical tokamak (ST). Ion temperatures in excess of 5keV have not previously been reached in any ST and have only been obtained in much larger devices with substantially more plasma heating power. The corresponding fusion triple product is calculated to be ni0Ti0τE≈6±2×1018m-3keVs. These results demonstrate for the first time that ion temperatures relevant for commercial magnetic confinement fusion can be obtained in a compact high-field spherical tokamak and bode well for fusion power plants based on the high-field ST.
Data‐driven models of human avatars have shown very accurate representations of static poses with soft‐tissue deformations. However they are not yet capable of precisely representing very nonlinear deformations and highly dynamic effects. Nonlinear skin mechanics are essential for a realistic depiction of animated avatars interacting with the environment, but controlling physics‐only solutions often results in a very complex parameterization task. In this work, we propose a hybrid model in which the soft‐tissue deformation of animated avatars is built as a combination of a data‐driven statistical model, which kinematically drives the animation, an FEM mechanical simulation. Our key contribution is the definition of deformation mechanics in a reference pose space by inverse skinning of the statistical model. This way, we retain as much as possible of the accurate static data‐driven deformation and use a custom anisotropic nonlinear material to accurately represent skin dynamics. Model parameters including the heterogeneous distribution of skin thickness and material properties are automatically optimized from 4D captures of humans showing soft‐tissue deformations.
Fig. 1. The left images show a dynamic simulation of an FEM Neo-Hookean jelly with 12,469 triangles. The deformation is rich but slow (20 fps). The central images show the same scene using a linear subspace model built with just 8 point handles. The simulation is fast (420 fps), but it misses all the detail and suffers distortion under moderate forces. The right images show the result with our model, which augments the linear model with nonlinear learning-based corrections. We retain fast dynamics close to the linear model (140 fps), but we recover the detailed contact-driven deformations of the full model.This paper introduces a novel subspace method for the simulation of dynamic deformations. The method augments existing linear handle-based subspace formulations with nonlinear learning-based corrections parameterized by the same subspace. Together, they produce a compact nonlinear model that combines the fast dynamics and overall contact-based interaction of subspace methods, with the highly detailed deformations of learning-based methods. We propose a formulation of the model with nonlinear corrections applied on the local undeformed setting, and decoupling internal and external contactdriven corrections. We define a simple mapping of these corrections to the global setting, an efficient implementation for dynamic simulation, and a training pipeline to generate examples that efficiently cover the interaction space. Altogether, the method achieves unprecedented combination of speed and contact-driven deformation detail.
Desde una investigación desarrollada en Bogotá (Colombia), se evidenció la importancia de fortalecer, durante la etapa lectiva, las competencias laborales del hacer en los contadores públicos. Allí surgió el objetivo de proponer un consultorio contable comunitario como estrategia pedagógica para atender esta necesidad y apoyar la inserción laboral de los estudiantes de contaduría. Mediante el método cualitativo, se abordó el fenómeno desde sus protagonistas, usando la hermenéutica contextual al triangular entrevistas, encuestas semiestructuradas y relatos emergentes. Se concluyó que las competencias del saber y del ser son fortalecidas mientras el estudiante desarrolla competencias del hacer en escenarios reales, en los cuales la inmersión resulta pertinente. La investigación, acudiendo a la autogestión universitaria, planteó una didáctica en contextos comunitarios que facilite la práctica en espacios reales y fortalezca el perfil laboral usando el voluntariado disciplinar; así, los contadores en formación se acercan a la práctica durante la etapa académica mediante la consultoría.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.