The quench of a binary mixture into a stable lamellar phase is numerically investigated. We present a novel approach to study the phenomenological equations governing the system dynamics based on the combination of lattice Boltzmann and finite difference equations. We can access large systems on time scales long enough to find evidence of dynamical scaling regime in the ordering process when hydrodynamics is operating.
In this article we continue our study on the complexity of Path Covering Problems started in [2]. Here, taking one further step, we investigate the complexity of the problem on grids. For special classes of grids (general grids, grids with a fixed number of rows, ladders), and several special unweighted path collections (general paths, paths of length 2, L-shaped paths, pipes, hooks, staples) we either give polynomial-time algorithms or prove NP-completeness results
Clustering has been one of the most popular methods to discover useful biological insights from DNA microarray. An interesting paradigm is simultaneous clustering of both genes and experiments. This "biclustering" paradigm aims at discovering clusters that consist of a subset of the genes showing a coherent expression pattern over a subset of conditions. The pClustering approach is a technique that belongs to this paradigm. Despite many theoretical advantages, this technique has been rarely applied to actual gene expression data analysis. Possible reasons include the worst-case complexity of the clustering algorithm and the difficulty in interpreting clustering results. In this paper, we propose an enhanced framework for performing pClustering on actual gene expression analysis. Our new framework includes an effective data preparation method, highly scalable clustering strategies, and an intuitive result interpretation scheme. The experimental result confirms the effectiveness of our approach.
