Relación de la escala de intensidad de Mercalli y la información instrumental como una tarea de clasificación de patrones

A pesar de los progresos ocurridos en la instrumentación sísmica, la valoración de vulnerabilidad sísmica y el daño con índices cualitativos, tal como los proporcionados por Intensidad de Mercalli Modificada (IMM), siguen siendo altamente favorables y útiles para los propósitos prácticos. Para vincular las medidas cualitativas de acción del terremoto y sus efectos, es habitualmente aplicada la regresión estadística. En este artículo, se adopta un planteamiento diferente, el cual consiste en expresar la Intensidad de Mercalli, como una clase en vez de un valor numérico. Una herramienta de clasificación estadística moderna, conocida como máquina de vectores de soporte, se usa para clasificar la información instrumental con el fin de evaluar la intensidad de Mercalli correspondiente. Se muestra que el método da resultados satisfactorios con respecto a las altas incertidumbres y a la medida del daño sísmico cualitativo

Semi-implicit Non-conforming Finite-Element Schemes for Cardiac Electrophysiology: A Framework for Mesh-Coarsening Heart Simulations

The field of computational cardiology has steadily progressed toward reliable and accurate simulations of the heart, showing great potential in clinical applications such as the optimization of cardiac interventions and the study of pro-arrhythmic effects of drugs in humans, among others. However, the computational effort demanded by in-silico studies of the heart remains challenging, highlighting the need of novel numerical methods that can improve the efficiency of simulations while targeting an acceptable accuracy. In this work, we propose a semi-implicit non-conforming finite-element scheme (SINCFES) suitable for cardiac electrophysiology simulations. The accuracy and efficiency of the proposed scheme are assessed by means of numerical simulations of the electrical excitation and propagation in regular and biventricular geometries. We show that the SINCFES allows for coarse-mesh simulations that reduce the computation time when compared to fine-mesh models while delivering wavefront shapes and conduction velocities that are more accurate than those predicted by traditional finite-element formulations based on the same coarse mesh, thus improving the accuracy-efficiency trade-off of cardiac simulations

Morphometric analysis of airways in pre-COPD and mild COPD lungs using continuous surface representations of the bronchial lumen

Introduction: Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory disease that presents a high rate of underdiagnosis during onset and early stages. Studies have shown that in mild COPD patients, remodeling of the small airways occurs concurrently with morphological changes in the proximal airways. Despite this evidence, the geometrical study of the airway tree from computed tomography (CT) lung images remains underexplored due to poor representations and limited tools to characterize the airway structure.Methods: We perform a comprehensive morphometric study of the proximal airways based on geometrical measures associated with the different airway generations. To this end, we leverage the geometric flexibility of the Snakes IsoGeometric Analysis method to accurately represent and characterize the airway luminal surface and volume informed by CT images of the respiratory tree. Based on this framework, we study the airway geometry of smoking pre-COPD and mild COPD individuals.Results: Our results show a significant difference between groups in airway volume, length, luminal eccentricity, minimum radius, and surface-area-to-volume ratio in the most distal airways.Discussion: Our findings suggest a higher degree of airway narrowing and collapse in COPD patients when compared to pre-COPD patients. We envision that our work has the potential to deliver a comprehensive tool for assessing morphological changes in airway geometry that take place in the early stages of COPD

Relación de la escala de intensidad de Mercalli y la información instrumental como una tarea de clasificación de patrones

A pesar de los progresos ocurridos en la instrumentación sísmica, la valoración de vulnerabilidad sísmica y el daño con índices cualitativos, tal como los proporcionados por Intensidad de Mercalli Modificada (IMM), siguen siendo altamente favorables y útiles para los propósitos prácticos. Para vincular las medidas cualitativas de acción del terremoto y sus efectos, es habitualmente aplicada la regresión estadística. En este artículo, se adopta un planteamiento diferente, el cual consiste en expresar la Intensidad de Mercalli, como una clase en vez de un valor numérico. Una herramienta de clasificación estadística moderna, conocida como máquina de vectores de soporte, se usa para clasificar la información instrumental con el fin de evaluar la intensidad de Mercalli correspondiente. Se muestra que el método da resultados satisfactorios con respecto a las altas incertidumbres y a la medida del daño sísmico cualitativo

WarpPINN: Cine-MR image registration with physics-informed neural networks

Heart failure is typically diagnosed with a global function assessment, such as ejection fraction. However, these metrics have low discriminate power, failing to distinguish different types of this disease. Quantifying local deformations in the form of cardiac strain can provide helpful information, but it remains a challenge. In this work, we introduce WarpPINN, a physics-informed neural network to perform image registration to obtain local metrics of the heart deformation. We apply this method to cine magnetic resonance images to estimate the motion during the cardiac cycle. We inform our neural network of near-incompressibility of cardiac tissue by penalizing the jacobian of the deformation field. The loss function has two components: an intensity-based similarity term between the reference and the warped template images, and a regularizer that represents the hyperelastic behavior of the tissue. The architecture of the neural network allows us to easily compute the strain via automatic differentiation to assess cardiac activity. We use Fourier feature mappings to overcome the spectral bias of neural networks, allowing us to capture discontinuities in the strain field. We test our algorithm on a synthetic example and on a cine-MRI benchmark of 15 healthy volunteers. We outperform current methodologies both landmark tracking and strain estimation. We expect that WarpPINN will enable more precise diagnostics of heart failure based on local deformation information. Source code is available at https://github.com/fsahli/WarpPINN.Comment: 18 pages, 10 figure

On improving the numerical convergence of highly nonlinear elasticity problems

Finite elasticity problems commonly include material and geometric nonlinearities and are solved using various numerical methods. However, for highly nonlinear problems, achieving convergence is relatively difficult and requires small load step sizes. In this work, we present a new method to transform the discretized governing equations so that the transformed problem has significantly reduced nonlinearity and, therefore, Newton solvers exhibit improved convergence properties. We study exponential-type nonlinearity in soft tissues and geometric nonlinearity in compression, and propose novel formulations for the two problems. We test the new formulations in several numerical examples and show significant reduction in iterations required for convergence, especially at large load steps. Notably, the proposed formulation is capable of yielding convergent solution even when 10–100 times larger load steps are applied. The proposed framework is generic and can be applied to other types of nonlinearities as well

Reactivity of bioinspired magnesium-organic networks under CO2 and O2 exposure

Photosynthesis is the model system for energy conversion. It uses CO2 as a starting reactant to convert solar energy into chemical energy, i.e., organic molecules or biomass. The first and rate-determining step of this cycle is the immobilization and activation of CO2, catalyzed by RuBisCO enzyme, the most abundant protein on earth. Here, we propose a strategy to develop novel biomimetic two-dimensional (2D) nanostructures for CO2 adsorption at room temperature by reductionist mimicking of the Mg-carboxylate RuBisCO active site. We present a method to synthesize a 2D surface-supported system based on Mg2+ centers stabilized by a carboxylate environment and track their structural dynamics and reactivity under either CO2 or O2 exposure at room temperature. The CO2 molecules adsorb temporarily on the Mg2+ centers, producing a charge imbalance that catalyzes a phase transition into a different configuration, whereas O2 adsorbs on the Mg2+ center, giving rise to a distortion in the metal-organic bonds that eventually leads to the collapse of the structure. The combination of bioinspired synthesis and surface reactivity studies demonstrated here for Mg-based 2D ionic networks holds promise for the development of new catalysts that can work at room temperature.Fil: Hurtado Salinas, Daniel E.. Ecole Polytechnique Federale de Lausanne; FranciaFil: Sarasola, Ane. Universidad del País Vasco; España. Donostia International Physics Center; EspañaFil: Stel, Bart. Ecole Polytechnique Federale de Lausanne; FranciaFil: Cometto, Fernando Pablo. Ecole Polytechnique Federale de Lausanne; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; ArgentinaFil: Kern, Klaus. Ecole Polytechnique Federale de Lausanne; Francia. Max Planck Institute For Solid State Research; AlemaniaFil: Arnau, Andrés. Universidad del País Vasco; EspañaFil: Lingenfelder, Magalí Alejandra. Swiss Federal Institute Of Technology Epfl, Lausanne; Suiz

Psiquiatría infantil: un tema en el olvido

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Psiquiatría infantil: un tema en el olvido

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