Impacts of Random Atomic Defects on Critical Buckling Stress of Graphene under Different Boundary Conditions.

Buckled graphene has potential applications in energy harvest, storage, conversion, and hydrogen storage. The investigation and quantification analysis of the random porosity in buckled graphene not only contributes to the performance reliability evaluation, but it also provides important references for artificial functionalization. This paper proposes a stochastic finite element model to quantify the randomly distributed porosities in pristine graphene.

Uncertainty Quantification for Epidemic Risk Management: Case of SARS-CoV-2 in Morocco.

In this paper, we propose a new method for epidemic risk modelling and prediction, based on uncertainty quantification (UQ) approaches. In UQ, we consider the state variables as members of a convenient separable Hilbert space, and we look for their representation in finite dimensional subspaces generated by truncations of a suitable Hilbert basis. The coefficients of the finite expansion can be determined by approaches established in the literature, adapted to the determination of the probability distribution of epidemic risk variables.

The Fingerprints of Resonant Frequency for Atomic Vacancy Defect Identification in Graphene.

The identification of atomic vacancy defects in graphene is an important and challenging issue, which involves inhomogeneous spatial randomness and requires high experimental conditions. In this paper, the fingerprints of resonant frequency for atomic vacancy defect identification are provided, based on the database of massive samples. Every possible atomic vacancy defect in the graphene lattice is considered and computed by the finite element model in sequence.

The correlation between graphene characteristic parameters and resonant frequencies by Monte Carlo based stochastic finite element model.

The uncertainty and fluctuations in graphene characteristic parameters are inevitable issues in both of experimental measurements and numerical investigations. In this paper, the correlations between characteristic parameters (Young's modulus, Poisson's ratio and thickness of graphene) and resonant frequencies are analyzed by the Monte Carlo based stochastic finite element model. Based on the Monte Carlo stochastic sampling procedure, the uncertainty in the characteristic parameters are properly propagated and quantified.

The Effects of Random Porosities in Resonant Frequencies of Graphene Based on the Monte Carlo Stochastic Finite Element Model.

With the distinguished properties in electronics, thermal conductivity, optical transparence and mechanics, graphene has a powerful potential in nanosensors, nano-resonators, supercapacitors, batteries, etc. The resonant frequency of graphene is an important factor in its application and working environment. However, the random dispersed porosities in graphene evidently change the lattice structure and destroy the integrity and geometrical periodicity.

Kriging Surrogate Model for Resonance Frequency Analysis of Dental Implants by a Latin Hypercube-Based Finite Element Method.

The dental implantation in clinical operations often encounters difficulties and challenges of failure in osseointegration, bone formulation, and remodeling. The resonance frequency (RF) can effectively describe the stability of the implant in physical experiments or numerical simulations. However, the exact relationship between the design variables of dental implants and RF of the system is correlated, complicated, and dependent.

A Self-Adaptive Umbrella Model for Vibration Analysis of Graphene.

The beam finite element and molecular dynamics models are two popular methods to represent the reaction of carbon-carbon bonds in graphene. However, the wrinkles and ripples in geometrical characteristics are difficult take into consideration. The out-planar mechanical properties are neglected in classical models of graphene.

Vibration Analysis of Vacancy Defected Graphene Sheets by Monte Carlo Based Finite Element Method.

The stochastic distributed placement of vacancy defects has evident effects on graphene mechanical property, which is a crucial and challenged issue in the field of nanomaterial. Different from the molecular dynamic theory and continuum mechanics theory, the Monte Carlo based finite element method (MC-FEM) was proposed and performed to simulate vibration behavior of vacancy defected graphene. Based on the Monte Carlo simulation, the difficulties in random distributed location of vacancy defects were well overcome.

[A nonlinear model for localization of hospital services as an indicator of accessibility].

The study proposes a differentiated approach to the localization of public services (unlike methods focusing solely on locational efficiency in the distribution of such services), with a nonlinear model that incorporates an accessibility indicator and allows rejecting solutions in which accessibility fails to comply with acceptably established minimum parameters. The method aims to minimize the total time spent by a region's population to reach a public services network, while controlling the range between the highest and lowest accessibility to the services. The resulting solution is not as efficient as other models (e.

[Assessment of geographic accessibility in hierarchical health systems using the p-median model: an application in Santa Catarina State, Brazil].

The objective of this study is to compare the distribution of hospital units according to different degrees of specialization in Santa Catarina State, Brazil, based on an application of the p-median hierarchical model in three levels. The p-median model is used to determine the units' location, following by a comparison of the population's mean distance to reach the medical units in two scenarios, the current one and the simulation. A quantitative indicator of accessibility is proposed and used to assess accessibility according to the current and simulated distributions.