Search results for "Monte Carlo."

Through-transmission laser welding of polymers – temperature field modeling and infrared investigation

2007

The purpose of the present study is to estimate the weldability of a polymeric material couple according to their thermal and optical properties. A first model based on Mie theory and Monte Carlo method describes the laser beam behavior in semi-transparent media and makes it possible to approximate the laser power distribution at the interface of the two materials. A second model based on finite element method permits the temperature field estimation into both parts to be welded. The results are validated by infrared thermography.

Onset of cohesion in cement paste

2004

It is generally agreed that the cohesion of cement paste occurs through the formation of a network of nanoparticles of a calcium-silicate-hydrate ("C-S-H"). However, the mechanism by which these particles develop this cohesion has not been established. Here we propose a dielectric continuum model which includes all ionic interactions within a dispersion of C-S-H particles. It takes into account all co-ions and counterions explicitly (with pure Coulomb interactions between ions and between ions and the surfaces) and makes no further assumptions concerning their hydration or their interactions with the surface sites. At high surface charge densities, the model shows that the surface charge of…

2016

We determine knotting probabilities and typical sizes of knots in double-stranded DNA for chains of up to half a million base pairs with computer simulations of a coarse-grained bead-stick model: Single trefoil knots and composite knots which include at least one trefoil as a prime factor are shown to be common in DNA chains exceeding 250,000 base pairs, assuming physiologically relevant salt conditions. The analysis is motivated by the emergence of DNA nanopore sequencing technology, as knots are a potential cause of erroneous nucleotide reads in nanopore sequencing devices and may severely limit read lengths in the foreseeable future. Even though our coarse-grained model is only based on …

Intermittent targeted therapies and stochastic evolution in patients affected by chronic myeloid leukemia

2016

Front line therapy for the treatment of patients affected by chronic myeloid leukemia (CML) is based on the administration of tyrosine kinase inhibitors, namely imatinib or, more recently, axitinib. Although imatinib is highly effective and represents an example of a successful molecular targeted therapy, the appearance of resistance is observed in a proportion of patients, especially those in advanced stages. In this work, we investigate the appearance of resistance in patients affected by CML, by modeling the evolutionary dynamics of cancerous cell populations in a simulated patient treated by an intermittent targeted therapy. We simulate, with the Monte Carlo method, the stochastic evolu…

Population pharmacokinetic meta-analysis of individual data to design the first randomized efficacy trial of vancomycin in neonates and young infants.

2019

Abstract Objectives In the absence of consensus, the present meta-analysis was performed to determine an optimal dosing regimen of vancomycin for neonates. Methods A ‘meta-model’ with 4894 concentrations from 1631 neonates was built using NONMEM, and Monte Carlo simulations were performed to design an optimal intermittent infusion, aiming to reach a target AUC0–24 of 400 mg·h/L at steady-state in at least 80% of neonates. Results A two-compartment model best fitted the data. Current weight, postmenstrual age (PMA) and serum creatinine were the significant covariates for CL. After model validation, simulations showed that a loading dose (25 mg/kg) and a maintenance dose (15 mg/kg q12h if &lt…

State transition identification in multivariate time series (STIMTS) applied to rotational jump trajectories from single molecules

2018

Time resolved data from single molecule experiments often suffer from contamination with noise due to a low signal level. Identifying a proper model to describe the data thus requires an approach with sufficient model parameters without misinterpreting the noise as relevant data. Here, we report on a generalized data evaluation process to extract states with piecewise constant signal level from simultaneously recorded multivariate data, typical for multichannel single molecule experiments. The method employs the minimum description length principle to avoid overfitting the data by using an objective function, which is based on a tradeoff between fitting accuracy and model complexity. We val…

Coarse-grained models of double-stranded DNA based on experimentally determined knotting probabilities

2018

Abstract To accurately model double-stranded DNA in a manner that is computationally efficient, coarse-grained models of DNA are introduced, where model parameters are selected by fitting the spectrum of observable DNA knots: We develop a general method to fit free parameters of coarse-grained chain models by comparing experimentally obtained knotting probabilities of short DNA chains to knotting probabilities that are computed in Monte Carlo simulations, resulting in coarse-grained DNA models which are tailored to reflect DNA topology in the best possible way. The method is exemplified by fitting ideal chain models as well as a bead-spring model with excluded volume interactions, to model …

Coupled conditional backward sampling particle filter

2020

The conditional particle filter (CPF) is a promising algorithm for general hidden Markov model smoothing. Empirical evidence suggests that the variant of CPF with backward sampling (CBPF) performs well even with long time series. Previous theoretical results have not been able to demonstrate the improvement brought by backward sampling, whereas we provide rates showing that CBPF can remain effective with a fixed number of particles independent of the time horizon. Our result is based on analysis of a new coupling of two CBPFs, the coupled conditional backward sampling particle filter (CCBPF). We show that CCBPF has good stability properties in the sense that with fixed number of particles, …

Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer

2019

AbstractA spatial Markov-chain model is formulated for the progression of skin cancer. The model is based on the division of the computational domain into nodal points, that can be in a binary state: either in ‘cancer state’ or in ‘non-cancer state’. The model assigns probabilities for the non-reversible transition from ‘non-cancer’ state to the ‘cancer state’ that depend on the states of the neighbouring nodes. The likelihood of transition further depends on the life burden intensity of the UV-rays that the skin is exposed to. The probabilistic nature of the process and the uncertainty in the input data is assessed by the use of Monte Carlo simulations. A good fit between experiments on mi…

Distributed channel prediction for multi-agent systems

2017

Los sistemas multiagente (MAS) se comunican a través de una red inalámbrica para coordinar sus acciones e informar sobre el estado de su misión. La conectividad y el rendimiento del sistema pueden mejorarse mediante la predicción de la ganancia del canal. Presentamos un esquema basado en regresión de procesos gaussianos (GPR) distribuidos para predecir el canal inalámbrico en términos de la potencia recibida en el MAS. El esquema combina una máquina de comité bayesiano con un esquema de consenso medio, distribuyendo así no sólo la memoria sino también la carga computacional y de comunicación. A través de simulaciones de Monte Carlo, demostramos el rendimiento del GPR propuesto. RACHEL TEC20…