论现代英语词汇的双层类聚模式

对于习得一种语言,词汇是很重要的一个基础。英语学习其中最重要的一个环节就是要多掌握词汇,而英语的词汇量很大,为了不断扩大英语词汇量,首先有必要弄清楚现代英语词汇的类聚状态和结构组织。

Modeling of the Multi-Target Locating and Tracking in the Field Artillery System

A method for the multi target locating and tracking with the multi sensor in a field artillery system is studied. A general modeling structure of the system is established. Based on concepts of cluster and closed ball, an algorithm is put forward for multi sensor multi target data fusion and an optimal solution for state estimation is presented. The simulation results prove the algorithm works well for the multi stationary target locating and the multi moving target tracking under the condition of the sparse target environment. Therefore, this method can be directly applied to the field artillery C 3I system.

Deterministic Secure Quantum Communication with Cluster State and Bell-Basis Measurements

We present a novel protocol for deterministic secure quantum communication by using the lour-qubit cluster state as quantum channel. It is shown that two legitimate users can directly transmit the secret messages based on Bellbasis measurements and classical communication. The present protocol makes use of the ideas of block transmission and decoy particle checking technique. It has a high capacity as each cluster state can carry two 5its of information, and has a high intrinsic efficieney 5ecause almost all the instances except the decoy checking particles （its numSer is negligible） are useful. Furthermore, this protocol is feasible with present-day technique.

Generation of Atomic Cluster State via Adiabatic Evolution of Dark Eigenstates

We propose a scheme to generate atomic cluster states of arbitrary configuration in the cavity quantumelectrodynamics (QED) system.The process is achieved via adiabatic evolution of dark states,which only requiresadiabatically increasing or decreasing Rabi frequencies of laser.Thus it allows the robust implementation of entanglementagainst certain types of errors.Our scheme is relatively decoherence-free in the sense that excited atomic states are neverpopulated and excited cavity photon states can be made negligible in certain conditions.

Application of different clustering approaches to hydroclimatological catchment regionalization in mountainous regions, a case study in Utah State

With respect to the different hydrological responses of catchments, even the adjacent ones, in mountainous regions, there are a great number of motivations for classifying them into homogeneous clusters. These motivations include prediction in ungauged basins(PUB), model parameterization, understanding the potential impact of environmental changes, transferring information from gauged catchments to the ungauged ones. The present study investigated the similarity of catchments through the hydro-climatological pure time-series of a 14-year period from 2001 to 2015. Data sets encompass more than 13,000 month-station streamflow, rainfall, and temperature data obtained from 27 catchments in Utah State as one of the eight mountainous states of the USA. The identification, analysis, and interpretation of homogeneous catchments were investigated by applying the four approaches ofclustering, K-means, Ward, and SOM(Self-Organized Map) and a newly proposed Wavelet-Entropy-based(WE-SOM) clustering method. By using two clustering evaluation criteria, 3, 5, and 6 clusters were determined as the best numbers of clusters, depending on the method employed, where each cluster represents different hydro-climatological behaviors. Despite the absence of geographic characteristics in input data matrix, the results indicated a regionalization in agreement with topographic characteristics. Considering the dependency of the hydrological behavior of catchments on the physiographic field aspects and characteristics, WE-SOM method demonstrated a more acceptable performance, compared to the other three conventional clustering methods, by providing more clusters. WE-SOM appears to be a promising approach in catchment clustering. It preserves the topological structure of data which can, as a result, be proofed in a greater number of clusters by dividing data into higher numbers of distinct clusters withsimilar altitudes of catchments in each cluster. The results showed the aptitude of wavelets to quantify the time-based variability of temperature, rainfall and streamflow, in the way contributing to the regionalization of diverse catchments.

Microwave Synthesis of Silver Nanoparticles Using Different Pentose Carbohydrates as Reducing Agents

A fast, green and readily reproducible microwave-based method for the production of high quality silver nanoparticles （AgNPs） in high yield is presented. Starch is used as a stabilizing agent with few pentose different reducing carbohydrates as D-ribose, D-arabinose and L-arabinose. From the UV-vis peak profile spectra of the solutions of the silver nanoparticles, the authors have investigated the size of the NPs together with the average diameter, shape, and aggregation state of the colloidal AgNPs. TEM measurements and EDX analysis have confirmed the morphology of our AgNPs.

Morphological Study of Ficus deltoidea Jack in Malaysia

Ficus deltoidea Jack （Moraceae） or Mas Cotek is a small shrub with a great morphological variation. Measurement of 40 morphological traits had been done on 50 accessions to find the most significant characters that enable differentiation being done according to its variety groups. The data were analyzed with principal component analysis （PCA） and cluster analysis （CA） using cluster software package programme to produce the scatter diagram and dendrogram relationship of the taxa. The results showed that there were 25 morphological characters having the value of factor analysis greater than 0.60 from its principal component （PC） with the Eigen value greater than 1.0. 16 out of 40 morphological characters had been identified as having high values of correlation coefficient ranging from -0.783 to 0.890. The analysis showed that the most responsible characters in grouping the samples into different groups are the shape and size of leaf, number and color of dots on the leaf surface and characters of syconium. The scatter diagram of the accessions on the PC1 against PC2 showed six major groups. The dendrogram displayed the relationship among the accessions and within the dissimilarity distance = 19, it classified the samples into five major groups. Observation on F. deltoidea resulted in the findings of high variability among the accessions. The most significant characters in grouping accessions are the shapes of leaf base （BL）, shape of leaf apex （SA）, ratio of lamina width to lamina length （R）, dots color at the lower midrib （DLM）, color of young syconium （CYS）, color of mature syconium （CMS） and the number of syconium on trees （DST）. This study provides basic information for introduction of some particular traits and effective conservation of the species breeding programme. The morphological traits were found to be useful for the diversity studies and in identifying the variation. The actual figures of F. deltoidea obtained through this study enable comparison being done to the previous and in future study. Hence, the varieties that are extinct could be recognised.

Clustering Algorithms to Analyze Molecular Dynamics Simulation Trajectories for Complex Chemical and Biological Systems

Molecular dynamics （MD） simulation has become a powerful tool to investigate the structure- function relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets con- taining millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape （geometric clustering） and kinetics （kinetic clustering）. In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, ag- glomerative algorithms （single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage）, center-based algorithms （K-Means, K-Medoids, K-Centers, and APM）, density-based algorithms （neighbor-based, DBSCAN, density-peaks, and Robust-DB）, and spectral-based algorithms （PCCA and PCCA＋）. In particular, differences between geomet- ric and kinetic clustering metrics will be discussed along with the performances of diflhrent clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algo- rithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.