MOTIVATION Monte Carlo methods are the most effective means of exploring the energy landscapes of protein folding. The rugged topography of folding energy landscapes causes sampling inefficiencies however, particularly at low, physiological temperatures. RESULTS A hybrid Monte Carlo method, termed density guided importance sampling (DGIS), is presented that overcomes these sampling inefficien...

background: the monte carlo method is the most accurate method for simulation of radiation therapy equipment. the linear accelerators are currently the most widely used machines in radiation therapy centers. methods: in this work, a monte carlo modelling of the siemens oncor linear accelerator in 6 mv and 18 mv beams was performed. the results of simulation were validated by measurements in wat...

In this work structural reliability assessment is presented for structures with uncertain loads and material properties. Uncertain variables are modeled as fuzzy random variables and Interval Monte Carlo Simulation along with interval finite element method is used to evaluate failure probability. Interval Monte Carlo is compared with existing search algorithms used in the reliability assessment...

background: megavoltage beams used in radiotherapy are contaminated with secondary electrons. different parts of linac head and air above patient act as a source of this contamination. this contamination can increase damage to skin and subcutaneous tissue during radiotherapy.  monte carlo simulation is an accurate method for dose calculation in medical dosimetry and has an important role in opt...

background: monte carlo method (mc) has played an important role in design and optimization of medical linacs head and beam modeling. the purpose of this study was to compare photon beam features of two commercial linacs, varian 21ex and elekta sl-25 using mcnp4c mc code. materials and methods: the 6mv photon beams of varian 21ex and elekta sl-25 linacs were simulated based on manufacturers pro...

Robust design optimization is a modeling methodology, combined with a suite of computational tools, which is aimed to solve problems where some kind of uncertainty occurs in the data or in the model. This paper explores robust optimization complexity in the multiobjective case, describing a new approach by means of Polynomial Chaos expansions (PCE). The aim of this paper is to demonstrate that ...

We present a computer-assisted approach to approximating coarse optimal switching policies for systems described by microscopic/stochastic evolution rules. The “coarse timestepper” constitutes a bridge between the underlying kinetic Monte Carlo simulation and traditional, continuum numerical optimization techniques formulated in discrete time. The approach is illustrated through a simplified ki...

Many post-secondary academic institutions in the United States have a First-Year Seminar Program. These seminars are designed to support the success of new incoming first-year students by combining writing, research and active discussion among small groups of students. At Dickinson College, students are required to select six seminars they find interesting from a list of approximately 42 semina...

As is typical of stochastic-optimization problems, the multivariate integration of the probability-density function is the most difficult task in the optimal allotment of tolerances. In this paper, a truncated Monte Carlo simulation and a genetic algorithm are used as analysis (i.e. multivariate-integration) and synthesis (i.e. optimization) tools, respectively. The new method has performed rob...