Mixtures of lipids and cholesterol are commonly used as model systems for studying the formation of liquid-ordered (Lo) domains in heterogeneous biological membranes. The simplest model system exhibiting coexistence b...
详细信息
Mixtures of lipids and cholesterol are commonly used as model systems for studying the formation of liquid-ordered (Lo) domains in heterogeneous biological membranes. The simplest model system exhibiting coexistence between Lo domains and a liquid-disordered (Ld) matrix is that of a binary mixture of saturated lipids such as DPPC and cholesterol (Chol). DPPC/Chol mixtures have been investigated for decades both experimentally, theoretically, and recently also by means of atomistic simulations. Here, we present a minimal lattice model that captures the correct behavior of this mixture across multiple scales. On the macroscopic scales, we present simulation results of mixtures of thousands of lipids and Chol molecules which show excellent agreement with the phase diagram of the system. The simulations are conducted on timescales of hundreds of microseconds and show the morphologies and dynamics of the domains. On the molecular scales, the simulations reveal local structures similar to those recently seen in atomistic simulations, including the formation of gel-like nanodomains (∼1–10 nm) within larger Chol-rich Lo domains (∼10–100 nm). The observed multiscale behavior is related to the tendency of Chol to induce ordering of acyl chains on the one hand, and disrupt their packing with each other, on the other hand.
K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a ...
详细信息
K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colony algorithm (CAABC) clustering algorithm is presented to optimally partition objects into K clusters in this study. This algorithm adopts the max-min distance product method for initialization. In addition, a new fitness function is adapted to the KMC algorithm. This paper also reports that the iteration abides by the adaptive search strategy, and Fuch chaotic disturbance is added to avoid converging on local optimum. The step length decreases linearly during the iteration. In order to overcome the shortcomings of the classic ABC algorithm, the simulated annealing criterion is introduced to the CAABC. Finally, the confluent algorithm is compared with other stochastic heuristic algorithms on the 20 standard test functions and 11 datasets. The results demonstrate that improvements in CAABA-K-means have an advantage on speed and accuracy of convergence over some conventional algorithms for solving clustering problems.
We study numerically external electric or magnetic field driven switching between percolated and non-percolated configuration of nanoparticles in soft matter ternary systems. The system consists of nematic liquid crys...
详细信息
We study numerically external electric or magnetic field driven switching between percolated and non-percolated configuration of nanoparticles in soft matter ternary systems. The system consists of nematic liquid crystal, impurities and elongated nanoparticles. We use the Lebwohl-Lasher lattice-type modeling to determine the orientational order of nanoparticles and consequently the metropolis algorithm to find the percolation threshold. In our model the external field acts directly only on the liquid crystal component, which in turn tends to reorient nanoparticles. We determine regimes where an external field relatively robustly switches between percolated and non-percolated states. The main variable physical parameters are volume concentration and length-to-width ratio of nanoparticles, concentration of impurities and temperature. We have revealed that impurities imposing static orientational disorder are a significant part of the system. A possible application is also proposed. (C) 2019 Elsevier B.V. All rights reserved.
We investigate popular trajectory-based algorithms inspired by biology and physics to answer a question of general significance: when is it beneficial to reject improvements? A distinguishing factor of SSWM (strong se...
详细信息
We investigate popular trajectory-based algorithms inspired by biology and physics to answer a question of general significance: when is it beneficial to reject improvements? A distinguishing factor of SSWM (strong selection weak mutation), a popular model from population genetics, compared to the metropolis algorithm (MA), is that the former can reject improvements, while the latter always accepts them. We investigate when one strategy outperforms the other. Since we prove that both algorithms converge to the same stationary distribution, we concentrate on identifying a class of functions inducing large mixing times, where the algorithms will outperform each other over a long period of time. The outcome of the analysis is the definition of a function where SSWM is efficient, while metropolis requires at least exponential time. The identified function favours algorithms that prefer high quality improvements over smaller ones, revealing similarities in the optimisation strategies of SSWM and metropolis respectively with best-improvement (BILS) and first-improvement (FILS) local search. We conclude the paper with a comparison of the performance of these algorithms and a (1,lambda) RLS on the identified function. The algorithm favours the steepest gradient with a probability that increases with the size of its offspring population. The results confirm that BILS excels and that the (1,lambda) RLS is efficient only for large enough population sizes.
Synthetic power grids enable real-world energy system simulations and are crucial for algorithm testing, resilience assessment, and policy formulation. We propose a novel method for the generation of synthetic transmi...
详细信息
Synthetic power grids enable real-world energy system simulations and are crucial for algorithm testing, resilience assessment, and policy formulation. We propose a novel method for the generation of synthetic transmission power grids using exponential random graph (ERG) models. Our two main contributions are (1) the formulation of an ERG model tailored specifically for capturing the topological nuances of power grids and (2) a general procedure for estimating the parameters of such a model conditioned on working with connected graphs. From a modeling perspective, we identify edge counts per bus type and k-triangles as crucial topological characteristics for synthetic power-grid generation. From a technical perspective, we develop a rigorous methodology to estimate the parameters of an ERG constrained to the space of connected graphs. The proposed model is flexible, easy to implement, and successfully captures the desired topological properties of power grids.
We consider metropolis-based systematic scan algorithms for generating Birman-Murakami-Wenzl (BMW) monoid basis elements of the BMW algebra. As the BMW monoid consists of tangle diagrams, these scanning strategies can...
详细信息
We consider metropolis-based systematic scan algorithms for generating Birman-Murakami-Wenzl (BMW) monoid basis elements of the BMW algebra. As the BMW monoid consists of tangle diagrams, these scanning strategies can be rephrased as random walks on links and tangles. We also consider the Brauer algebra and use metropolis-based scans to generate Brauer diagrams, giving rise to random walks on perfect matchings. Taking an algebraic perspective, we translate these walks into left multiplication operators in the BMW algebra and so give an algebraic interpretation of the metropolis algorithm in this setting.
Assessment of failure probability, especially for a complex structure, requires a considerable number of calls to the numerical model. Reliability methods have been developed to decrease the computational time. In thi...
详细信息
Assessment of failure probability, especially for a complex structure, requires a considerable number of calls to the numerical model. Reliability methods have been developed to decrease the computational time. In this approach, the original numerical model is replaced by a surrogate model which is usually explicit and much faster to evaluate. The current paper proposed an efficient reliability method based on Monte Carlo simulation (MCS) and multi-gene genetic programming (MGGP) as a robust variant of genetic programming (GP). GP has been applied in different fields;however, its application to structural reliability has not been tested. The current study investigated the performance of MGGP as a surrogate model in structural reliability problems and compares it with other surrogate models. An adaptive metropolis algorithm is utilized to obtain the training data with which to build the MGGP model. The failure probability is estimated by combining MCS and MGGP. The efficiency and accuracy of the proposed method were investigated with the help of five numerical examples.
We critically analyze a recent numerical method due to the first author, Rechnitzer, and van Rensburg, which attempts to detect amenability in a finitely generated group by numerically estimating its asymptotic cogrow...
详细信息
We critically analyze a recent numerical method due to the first author, Rechnitzer, and van Rensburg, which attempts to detect amenability in a finitely generated group by numerically estimating its asymptotic cogrowth rate. We identify two potential sources of error. We then propose a modification of the method that enables it to easily compute surprisingly accurate estimates for initial terms of the cogrowth sequence.
We investigate the problem of quantifying contraction coefficients of Markov transition kernels in Kantorovich (L-1 Wasserstein) distances. For diffusion processes, relatively precise quantitative bounds on contractio...
详细信息
We investigate the problem of quantifying contraction coefficients of Markov transition kernels in Kantorovich (L-1 Wasserstein) distances. For diffusion processes, relatively precise quantitative bounds on contraction rates have recently been derived by combining appropriate couplings with carefully designed Kantorovich distances. In this paper, we partially carry over this approach from diffusions to Markov chains. We derive quantitative lower bounds on contraction rates for Markov chains on general state spaces that are powerful if the dynamics is dominated by small local moves. For Markov chains on R(d )with isotropic transition kernels, the general bounds can be used efficiently together with a coupling that combines maximal and reflection coupling. The results are applied to Euler discretizations of stochastic differential equations with non-globally contractive drifts, and to the metropolis adjusted Langevin algorithm for sampling from a class of probability measures on high dimensional state spaces that are not globally log-concave.
We study a two-dimensional ferromagnetic Ising model in which spins are updated using modified versions of the metropolis and Glauber algorithms. These update rules do not obey the detailed balance condition. The stea...
详细信息
We study a two-dimensional ferromagnetic Ising model in which spins are updated using modified versions of the metropolis and Glauber algorithms. These update rules do not obey the detailed balance condition. The steady-state behavior of the model is studied using molecular field theory and Monte Carlo simulations. This model is found to exhibit a nonequilibrium phase transition from a “paramagnetic” state with zero magnetization to a “ferromagnetic” state with nonzero magnetization as the variable that plays the role of temperature in the spin updates is decreased. From detailed Monte Carlo simulations using the modified metropolis algorithm, we demonstrate explicitly the nonequilibrium nature of the transition and show that it cannot be described as an equilibrium transition with an effective temperature different from the temperature used in the spin updates. The critical exponents that characterize the singular behavior near this continuous phase transition are calculated from finite size scaling of specific heat, magnetization, susceptibility, and correlation length. We find that the values of these exponents are the same (within error bars) as those of the equilibrium Ising model in two dimensions.
暂无评论