For much of the last three decades, Monte Carlo-simulation methods have been the standard approach for accurately calculating the cyclization probability, J, or J factor, for DNA models having sequence-dependent bends or inhomogeneous bending flexibility. Within the last 10 years approaches based on harmonic analysis of semi-flexible polymer models have been introduced, which offer much greater...

Monte Carlo simulation is a very important class of stochastic methods for calculating thermal properties of many-particle systems—arguably these are the most important numerical techniques in statistical physics. Monte Carlo simulation methods are related to the elementary Monte Carlo integration methods that we discussed earlier, but are based on more efficient non-uniform sampling schemes. B...

In recent years, the quasi-Monte Carlo approach for pricing high-dimensional derivative securities has been used widely relative to other competitive approaches such as the Monte Carlo methods. Such success can be, in part, attributed to the notion of effective dimension of the finance problems. In this paper, we provide additional insight on the connection between the effective dimension and t...

the present paper offers a meso-scale numerical model to investigate the effects of random distribution of aggregate particles and steel fibers on the elastic modulus of steel fiber reinforced concrete (sfrc). meso-scale model distinctively models coarse aggregate, cementitious mortar, and interfacial transition zone (itz) between aggregate, mortar, and steel fibers with their respective materi...

In this paper we describe a hybrid deterministic/Monte Carlo algorithm for neutron transport simulation. The algorithm is based on nonlinear accelerators for source iteration, using Monte Carlo methods for the purely absorbing high-order problem and a Jacobian-free Newton– Krylov iteration for the low-order problem. We couple the Monte Carlo solution with the low-order problem using filtering t...

In economics, several parametric regression-based models have been proposed to measure the technical efficiency of decision making units (DMUs). However, the problem of misspecification restricts the use of these methods. In this paper, symbolic regression is employed to obtain the approximate optimal production function automatically using genetic programming (GP). Monte Carlo simulation is us...

Monte Carlo simulation is a very important class of stochastic methods for calculating thermal properties of many-particle systems—arguably these are the most important numerical techniques in statistical physics. Monte Carlo simulation methods are related to the elementary Monte Carlo integration methods that we discussed earlier, but are based on more efficient non-uniform sampling schemes. B...

We study the global solution of Fredholm integral equations of the second kind by the help of Monte Carlo methods. Global solution means that we seek to approximate the full solution function. This is opposed to the usual applications of Monte Carlo, were one only wants to approximate a functional of the solution. In recent years several researchers developed Monte Carlo methods also for the gl...

Introduction: The aim of this study was to investigate the initial electron beam parameters for Monte Carlo model of 6MV photon beam produced by Siemens Oncor® linear accelerator.   Materials and Methods: In this study, the EGSnrc Monte Carlo user codes BEAMnrc and DOSXYZnrc were used. The beamnrc code were used for modelling of a 6 MV photon beam produced by...

This paper proposes a Monte Carlo algorithm for gammaphoton transport, that partially reuses random paths and is appropriate for parallel GPU implementation. According to the requirements of the application of the simulation results in reconstruction algorithms, the method aims at similar relative rather than absolute errors of the detectors. The resulting algorithm is SIMD-like, which is a req...