Dynamic “Molecular Portraits” of Biomembranes Drawn by Their Lateral Nanoscale Inhomogeneities

To date, it has been reliably shown that the lipid bilayer/water interface can be thoroughly characterized by a sophisticated so-called “dynamic molecular portrait”. The latter reflects a combination of time-dependent surface distributions of various physicochemical properties, inherent in both model lipid bilayers and natural multi-component cell membranes. One of the most important features of biomembranes is their mosaicity, which is expressed in the constant presence of lateral inhomogeneities, the sizes and lifetimes of which vary in a wide range—from 1 to 10³ nm and from 0.1 ns to milliseconds. In addition to the relatively well-studied macroscopic domains (so-called “rafts”), the analysis of micro- and nanoclusters (or domains) that form an instantaneous picture of the distribution of structural, dynamic, hydrophobic, electrical, etc., properties at the membrane-water interface is attracting increasing interest. This is because such nanodomains (NDs) have been proven to be crucial for the proper membrane functioning in cells. Therefore, an understanding with atomistic details the phenomena associated with NDs is required. The present mini-review describes the recent results of experimental and in silico studies of spontaneously formed NDs in lipid membranes. The main attention is paid to the methods of ND detection, characterization of their spatiotemporal parameters, the elucidation of the molecular mechanisms of their formation. Biological role of NDs in cell membranes is briefly discussed. Understanding such effects creates the basis for rational design of new prospective drugs, therapeutic approaches, and artificial membrane materials with specified properties.     

纯铜动态再结晶的Monte Carlo法模拟

本文建立了一种基于能量判据的确定动态再结晶各个阶段临界应变的方法，并基于此种方法Monte Carlo(MC)法进行了纯铜动态再结晶的二维模拟，定量计算了变形引起的存储能与变形之间的关系，计算出了发生动态再结晶的临界应变。模拟结果表明，采用MC法可以揭示动态再结晶的形核生长过程与应变历史之间的关系。结合本文所建立的基于能量判据的确定动态再结晶各个阶段临界应变的方法，可以预测开始发生动态再结晶的临界变形条件。     

Development of the semi-empirical equation of state for square-well chain fluid based on the Statistical Associating Fluid Theory (SAFT)

A semi-empirical equation of state for the freely jointed square-well chain fluid is developed. This equation of state is based on Wertheim’s thermodynamic perturbation theory (TPT) and the statistical associating fluid theory (SAFT). The compressibility factor and radial distribution function of square-well monomer are obtained from Monte Carlo simulations. These results are correlated using density expansion. In developing the equation of state the exact analytical expressions are adopted for the second and third virial coefficients for the compressibility factor and the first two terms of the radial distribution function, while the higher order coefficients are determined from regression using the simulation data. In the limit of infinite temperature, the present equation of state and the expression for the radial distribution function are represented by the Carnahan-Starling equation of state. This semi-empirical equation of state gives at least comparable accuracy with other empirical equation of state for the square-well monomer fluid. With the new SAFT equation of state from the accurate expressions for the monomer reference and covalent terms, we compare the prediction of the equation of state to the simulation results for the compressibility factor and radial distribution function of the square-well monomer and chain fluids. The predicted compressibility factors for square well chains are found to be in a good agreement with simulation data. The high accuracy of the present equation of state is ascribed to the fact that rigorous simulation results for the reference fluid are used, especially at low temperatures and low densities. This paper was presented at the 8th APCChE (Asia Pacific Confederation of Chemical Engineering) Congress held at Seoul between August 16 and 19, 1999.     

Janus II: A new generation application-driven computer for spin-system simulations

This paper describes the architecture, the development and the implementation of Janus II, a new generation application-driven number cruncher optimized for Monte Carlo simulations of spin systems (mainly spin glasses). This domain of computational physics is a recognized grand challenge of high-performance computing: the resources necessary to study in detail theoretical models that can make contact with experimental data are by far beyond those available using commodity computer systems. On the other hand, several specific features of the associated algorithms suggest that unconventional computer architectures–that can be implemented with available electronics technologies–may lead to order of magnitude increases in performance, reducing to acceptable values on human scales the time needed to carry out simulation campaigns that would take centuries on commercially available machines. Janus II is one such machine, recently developed and commissioned, that builds upon and improves on the successful JANUS machine, which has been used for physics since 2008 and is still in operation today. This paper describes in detail the motivations behind the project, the computational requirements, the architecture and the implementation of this new machine and compares its expected performances with those of currently available commercial systems.     

CUDA programs for the GPU computing of the Swendsen–Wang multi-cluster spin flip algorithm: 2D and 3D Ising,Potts, and XY models

We present sample CUDA programs for the GPU computing of the Swendsen–Wang multi-cluster spin flip algorithm. We deal with the classical spin models; the Ising model, the q$q$-state Potts model, and the classical XY model. As for the lattice, both the 2D (square) lattice and the 3D (simple cubic) lattice are treated. We already reported the idea of the GPU implementation for 2D models (Komura and Okabe, 2012). We here explain the details of sample programs, and discuss the performance of the present GPU implementation for the 3D Ising and XY models. We also show the calculated results of the moment ratio for these models, and discuss phase transitions.     

PRAND: GPU accelerated parallel random number generation library: Using most reliable algorithms and applying parallelism of modern GPUs and CPUs

The library PRAND for pseudorandom number generation for modern CPUs and GPUs is presented. It contains both single-threaded and multi-threaded realizations of a number of modern and most reliable generators recently proposed and studied in Barash (2011), Matsumoto and Tishimura (1998), L’Ecuyer (1999,1999), Barash and Shchur (2006) and the efficient SIMD realizations proposed in Barash and Shchur (2011). One of the useful features for using PRAND in parallel simulations is the ability to initialize up to 10¹⁹${10}^{19}$ independent streams. Using massive parallelism of modern GPUs and SIMD parallelism of modern CPUs substantially improves performance of the generators.     

Application of an improved control-variate scheme to local neoclassical transport simulations

A new radially local neoclassical transport code is developed based on the radially global two-weight δf$\delta f$ Monte Carlo code, FORTEC-3D (Satake et al., 2008). In the collisional two-weight δf$\delta f$ method, the variance of weight increases in time due to the so-called weight spreading, which leads to an increasing numerical noise in long time simulations. A new improved control-variate scheme was proposed by Kleiber et al. (2011) to reduce the variance. We investigate the effectiveness and validity of the new control-variate scheme for a practical collisional transport problem in a plasma described by the drift kinetic equation. It is demonstrated that the new scheme reduces the variance of weight by approximately 75% in an axisymmetric magnetic field configuration. The burst-like behavior of a transport observable caused by a large numerical noise can be avoided. As a result, the time evolution of a transport observable can be successfully smoothed.     

Computer simulation of the filling process in gas‐assisted injection molding based on gas‐penetration modeling

Gas‐assisted injection molding can effectively produce parts free of sink marks in thick sections and free of warpage in long plates. This article concerns the numerical simulation of melt flow and gas penetration during the filling stage in gas‐assisted injection molding. By taking the influence of gas penetration on the melt flow as boundary conditions of the melt‐filling region, a hybrid finite‐element/finite‐difference method similar to conventional‐injection molding simulation was used in the gas‐assisted injection molding‐filling simulation. For gas penetration within the gas channel, an analytical formulation of the gas‐penetration thickness ratio was deduced based on the matching asymptotic expansion method. Finally, an experiment was employed to verify this proposed simulation scheme and gas‐penetration model, by comparing the results of the experiment with the simulation. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 90: 2377–2384, 2003     

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.