Generation of linear and nonlinear waves in numerical wave tank using clustering technique-volume of fluid method

A two-dimensional (2D) numerical model is developed for the wave simulation and propagation in a wave flume.The fluid flow is assumed to be viscous and incompressible,and the Navier-Stokes and continui...     

聚类分析法在地质多样性资源评价中的应用——以湖北省大别山地区为例

对地质多样性的概念、内容体系、价值、国内外研究现状等进行概述,根据地质多样性内容建立评价指标体系；并用聚类分析法对湖北省大别山地区的地质多样性资源进行评价,得出地质多样性资源分布呈纬度地带性规律.     

基础教育数学物理化学教材用字用词调查（二）

为了更好地了解语言学习、语言能力培养与学科知识获取之间的关系，语言学习、语言能力培养与学科知识获取之间的关系，论文统计了基础教育阶段的数学、物理、化学三科教材中汉字、词语的使用情况，分析了它们的字词的种数、使用频率数、与语文科教材的用字用词的共见与独见情况。发现它们的字种数、词种数及总词次较少，复现率较低主要由以下四类构成：1．学科术语；2．一般性的语文字词；3．指称日常生活中一些特定的物、人、事的字词；4．教学类字词。     

Handling uncertainties in toxicity modelling using a fuzzy filter

A fundamental concern in the Quantitative Structure-Activity Relationship approach to toxicity evaluation is the generalization of the model over a wide range of compounds. The data driven modelling of toxicity, due to the complex and ill-defined nature of eco-toxicological systems, is an uncertain process. The development of a toxicity predicting model without considering uncertainties may produce a model with a low generalization performance. This study presents a novel approach to toxicity modelling that handles the involved uncertainties using a fuzzy filter, and thus improves the generalization capability of the model. The method is illustrated by considering a data set dealing with the fathead minnow (Pimephales promelas) toxicity of 568 organic compounds.     

黄渤海近岸海域酚类内分泌干扰物分布特征及其来源解析

在黄渤海近岸海域采集了34个水体样品,利用HPLC-MS/MS分析了双酚A、辛基酚、壬基酚、2,4-二氯酚、对叔丁基苯酚和对特辛基苯酚等6种酚类内分泌干扰物的含量,并探讨了其分布特征及来源.结果表明,中国北部近岸海域6种酚类内分泌干扰物的含量范围在5.25～1351.20ng/mL之间.结合因子分析和层次聚类分析结果,说明渤海、黄海近岸海域中酚类化合物主要以辛基酚、壬基酚、2,4-二氯酚为主,局部海域伴有双酚A的高残留;从整个海域范围看,黄渤海近岸海域水体中酚类化合物污染状况具有区域特征,整体呈现出南高北低的特点,且酚类物质分布具有明显的地区特性,一定程度上具有聚集性;来源解析结果表明黄渤海近岸海域中酚类内分泌干扰物主要来源为生活污水和工业废水.     

A fast parallel clustering algorithm for molecular simulation trajectories

We implemented a GPU‐powered parallel k‐centers algorithm to perform clustering on the conformations of molecular dynamics (MD) simulations. The algorithm is up to two orders of magnitude faster than the CPU implementation. We tested our algorithm on four protein MD simulation datasets ranging from the small Alanine Dipeptide to a 370‐residue Maltose Binding Protein (MBP). It is capable of grouping 250,000 conformations of the MBP into 4000 clusters within 40 seconds. To achieve this, we effectively parallelized the code on the GPU and utilize the triangle inequality of metric spaces. Furthermore, the algorithm's running time is linear with respect to the number of cluster centers. In addition, we found the triangle inequality to be less effective in higher dimensions and provide a mathematical rationale. Finally, using Alanine Dipeptide as an example, we show a strong correlation between cluster populations resulting from the k‐centers algorithm and the underlying density. © 2012 Wiley Periodicals, Inc.     

回归的能源有效网络大数据流汇聚算法研究

为了降低传感器网络数据流汇聚时的能源消耗,提出了一种基于回归的能源有效数据流汇聚算法。首先,将传感器节点分为活跃节点和能源有效节点。然后,以活跃节点为中心点将所有节点进行聚类,并应用回归方法通过活跃节点的数据流对能源有效节点的数据进行预测。接下来,通过节点预测值的累积误差不断修正活跃节点集。最后,应用活跃节点的数据流信息对能源有效节点的数据进行预测。实验表明,本文提出的算法与其它相关算法相比具有更好的预测准确性。     

Rapid determination of RMSDs corresponding to macromolecular rigid body motions

Finding the root mean sum of squared deviations (RMSDs) between two coordinate vectors that correspond to the rigid body motion of a macromolecule is an important problem in structural bioinformatics, computational chemistry, and molecular modeling. Standard algorithms compute the RMSD with time proportional to the number of atoms in the molecule. Here, we present RigidRMSD, a new algorithm that determines a set of RMSDs corresponding to a set of rigid body motions of a macromolecule in constant time with respect to the number of atoms in the molecule. Our algorithm is particularly useful for rigid body modeling applications, such as rigid body docking, and also for high‐throughput analysis of rigid body modeling and simulation results. We also introduce a constant‐time rotation RMSD as a similarity measure for rigid molecules. A C++ implementation of our algorithm is available at http://nano‐d.inrialpes.fr/software/RigidRMSD . © 2014 Wiley Periodicals, Inc.     

基于用户移动行为相似性聚类的Markov位置预测

由于采集点丢失或出现新用户等原因,GPS轨迹数据往往具有稀疏性,使得基于单个用户数据的位置预测准确率较低.针对这种情况,提出了基于移动行为相似性和用户聚类的Markov位置预测方法.首先,基于Voronoi图和原始GPS轨迹进行区域划分,位置预测基于区域轨迹进行;其次,提出了同时考虑用户转移特性和用户区域特性的移动行为相似性计算方法;再次,根据移动行为相似性对用户进行聚类,并在聚类的用户组上采用一阶Markov模型进行位置预测,提高了位置预测的准确性.真实GPS轨迹数据上的实验表明了所提出方法的有效性.