电子显微镜分析北京雨雪中不可溶成分

1 994～ 1 995年的冬季和夏季 ,在北京大学共观测了 1 1次不同类型的雨、雪过程 ,对每次降水过程进行连续取样和过滤 ,用电子显微镜分析全部 2 8个降水过滤样品 ,测定不可溶成分中 1 5种元素 (Si、Fe、Al、K、Ca、Cu、Ti、Mg、Na、P、S、Cl、Ni、Mn、Cr)的重量百分比分布。通过平均值计算、聚类法分析和元素间的相关分析 ,讨论了降水中不可溶成分的物质组成和来源。

Prediction method of highway pavement rutting based on the grey theory

In order to make a scientific pavement maintenance decision, a grey-theory-based prediction methodological framework is proposed to predict pavement performance. Based on the field pavement rutting data,analysis of variance （ANOVA）was first used to study the influence of different factors on pavement rutting. Cluster analysis was then employed to investigate the rutting development trend.Based on the clustering results,the grey theory was applied to build pavement rutting models for each cluster, which can effectively reduce the complexity of the predictive model.The results show that axial load and asphalt binder type play important roles in rutting development.The prediction model is capable of capturing the uncertainty in the pavement performance prediction process and can meet the requirements of highway pavement maintenance,and,therefore,has a wide application prospects.

Clustering analysis algorithm for security supervising data based on semantic description in coal mines

In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm.

秦皇岛市地表水环境监测点位优化研究

随着经济社会发展,秦皇岛水环境监测点位存在局部布设不合理现象。有必要针对环境所需,对原有的监测点位优化设置,使其更具有科学性和代表性。在查阅了大量有关资料的基础上,结合实际工作情况,搜集秦皇岛2008—2012年5年的地表水环境监测数据,对秦皇岛市主要地表水环境监测的7条河流的25个监测断面进行筛选分类,根据实际需要在保留原有省级监控断面的基础上,建议增加削减断面和对照断面。

DNA Sequence Classification Based on the Side Chain Radical Polarity of Amino Acids

The features of DNA sequence fragments were extracted from the distribution density of the condons in the individual cases of DNA sequence fragments. Based on the polarity of side chain radicals of amino acids molecules, the amino acids were classified into five categories, and the frequencies of these five categories were calculated. This kind of feature extraction based on the biological meanings not only took the content of basic groups into consideration, but also considered the marshal ing sequence of the basic groups. The hierarchical clustering analysis and BP neural network were used to classify the DNA sequence fragments. The results showed that the classification results of these two kinds of algo-rithms not only had high accuracy, but also had high consistence, indicating that this kind of feature extraction was superior over the traditional feature extraction which only took the features of basic groups into consideration.

An efficient enhanced k-means clustering algorithm

In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this paper, we present a simple and efficient clustering algorithm based on the k-means algorithm, which we call enhanced k-means algorithm. This algorithm is easy to implement, requiring a simple data structure to keep some information in each iteration to be used in the next iteration. Our experimental results demonstrated that our scheme can improve the computational speed of the k-means algorithm by the magnitude in the total number of distance calculations and the overall time of computation.

A SPATIAL CLUSTER METHOD SUPPORTED BY GIS FOR URBAN-SUBURBAN-RURAL CLASSIFICATION

This study was undertaken to construct a preliminary spatial analysis method for building an urban-suburban-rural category in the specific sample area of central California and providing distribution characteristics in each category, based on which, some further studies such as regional manners of residential wood burning emission (PM2.5, the term used for a mixture of solid particles and liquid droplets found in the air, refers to particulate matter that is 2.5 mu m or smaller in size) could be carried out for the project of residential wood combustion. Demographic and infrastructure data with spatial characteristics were processed by integrating both Geographic Information System (GIS) and statistics method (Cluster Analysis), and then output to a category map as the result. It approached the quantitative and multi-variables description on the major characteristics variations among the urban, suburban and rural; and perfected the TIGER's urban-rural classification scheme by adding suburban category. Based on the free public GIS data, the spatial analysis method provides an easy and ideal tool for geographic researchers, environmental planners, urban/regional planners and administrators to delineate different categories of regional function on the specific locations and dig out spatial distribution information they wanted. Furthermore, it allows for future adjustment on some parameters as the spatial analysis method is implemented in the different regions or various eco-social models.

Multi-resolution graph-based clustering analysis for lithofacies identifi cation from well log data: Case study of intraplatform bank gas fi elds, Amu Darya Basin

In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.

Numerical simulation study of the failure evolution process and failure mode of surrounding rock in deep soft rock roadways

Based on the safety coefficient method,which assigns rock failure criteria to calculate the rock mass unit,the safety coefficient contour of surrounding rock is plotted to judge the distribution form of the fractured zone in the roadway.This will provide the basis numerical simulation to calculate the surrounding rock fractured zone in a roadway.Using the single factor and multi-factor orthogonal test method,the evolution law of roadway surrounding rock displacements,plastic zone and stress distribution under different conditions is studied.It reveals the roadway surrounding rock burst evolution process,and obtains five kinds of failure modes in deep soft rock roadway.Using the fuzzy mathematics clustering analysis method,the deep soft surrounding rock failure model in Zhujixi mine can be classified and patterns recognized.Compared to the identification results and the results detected by geological radar of surrounding rock loose circle,the reliability of the results of the pattern recognition is verified and lays the foundations for the support design of deep soft rock roadways.

MR-CLOPE: A Map Reduce based transactional clustering algorithm for DNS query log analysis

DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE.

Seasonal variation of the Taiwan Warm Current Water and its underlying mechanism

Based on the historical observed data and the modeling results,this paper investigated the seasonal variations in the Taiwan Warm Current Water(TWCW)using a cluster analysis method and examined the contributions of the Kuroshio onshore intrusion and the Taiwan Strait Warm Current(TSWC)to the TWCW on seasonal time scales.The TWCW has obviously seasonal variation in its horizontal distribution,T-S characteristics and volume.The volume of TWCW is maximum(13746 km^3)in winter and minimum(11397 km^3)in autumn.As to the contributions to the TWCW,the TSWC is greatest in summer and smallest in winter,while the Kuroshio onshore intrusion northeast of Taiwan Island is strongest in winter and weakest in summer.By comparison,the Kuroshio onshore intrusion make greater contributions to the Taiwan Warm Current Surface Water(TWCSW)than the TSWC for most of the year,except for in the summertime(from June to August),while the Kuroshio Subsurface Water(KSSW)dominate the Taiwan Warm Current Deep Water(TWCDW).The analysis results demonstrate that the local monsoon winds is the dominant factor controlling the seasonal variation in the TWCW volume via Ekman dynamics,while the surface heat fl ux can play a secondary role via the joint ef fect of baroclinicity and relief.

Community characteristics of macrobenthos community in Changli Gold Coast National Nature Reserve of Hebei in China

Macrobenthic communities in the surrounding waters of Changli were investigated during spring and summer in2016.Differences in species quantity,abundance and biomass,the dominant species and species diversity index of macrobenthos were analyzed.The results showed that58macrobenthos species were identified in spring,and92macrobenthos species were identified in summer.The composition of dominant species seasonally varied;however,most of them were species belonging to Polychaeta.The abundance of macrobenthos in summer was slightly higher than that in spring,while the biomass in summer was significantly smaller than that in spring.Bray-Curtis cluster analysis and multi-dimentional scaling(MDS)analysis indicated that macrobenthic communities were divided into three communities in spring,and two in summer.The abundance-biomass comparison(ABC)curve method was used to monitor the disturbance of environmental pollution for macrobenthic community.The results showed that the macrobenthos in this area received serious disturbance from environmental pollution.

A self region based real-valued negative selection algorithm

Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection rate.To solve this problem,a self region based real-valued negative selection algorithm is presented.In this new approach,the continuous self region is defined by the collection of self data,the partial training takes place at the training stage according to both the radius of self region and the cosine distance between gravity of the self region and detector candidate,and variable detectors in the self region are deployed.The algorithm is tested using the triangle shape of self region in the 2-D complement space and KDD CUP 1999 data set.Results show that,more information can be provided when the training self points are used together as a whole,and compared with the point-wise negative selection algorithm,the new approach can improve the training efficiency of system and the detection rate significantly.

Combined Density-based and Constraint-based Algorithm for Clustering

We propose a new clustering algorithm that assists the researchers to quickly and accurately analyze data. We call this algorithm Combined Density-based and Constraint-based Algorithm (CDC). CDC consists of two phases. In the first phase, CDC employs the idea of density-based clustering algorithm to split the original data into a number of fragmented clusters. At the same time, CDC cuts off the noises and outliers. In the second phase, CDC employs the concept of K-means clustering algorithm to select a greater cluster to be the center. Then, the greater cluster merges some smaller clusters which satisfy some constraint rules. Due to the merged clusters around the center cluster, the clustering results show high accuracy. Moreover, CDC reduces the calculations and speeds up the clustering process. In this paper, the accuracy of CDC is evaluated and compared with those of K-means, hierarchical clustering, and the genetic clustering algorithm (GCA) proposed in 2004. Experimental results show that CDC has better performance.

Matrix metalloproteinase-2 as a superior biomarker for peritoneal deterioration in peritoneal dialysis

AIM： To investigate the effcacy of effuent biomarkers for peritoneal deterioration with functional decline in peritoneal dialysis （PD）.METHODS： From January 2005 to March 2013, the subjects included 218 PD patients with end-stage renal disease at 18 centers. Matrix metalloproteinase-2 （MMP-2）, interleukin-6 （IL-6）, hyaluronan, and cancer antigen 125 （CA125） in peritoneal effluent were quantified with enzyme-linked immunosorbent assay. Peritoneal solute transport rate was assessed by peritoneal equilibration test （PET） to estimate peritoneal deterioration.RESULTS： The ratio of the effuent level of creatinine （Cr） obtained 4 h after injection （D） to that of plasma was correlated with the effluent levels of MMP-2 （ρ = 0.74, P 〈 0.001）, IL-6 （ρ = 0.46, P 〈 0.001）, and hyaluronan （ρ = 0.27, P 〈 0.001）, but not CA125 （ρ = 0.13, P = 0.051）. The area under receiver operating characteristic curve for the effluent levels of MMP-2, IL-6, and hyaluronan against high PET category were 0.90, 0.78, 0.62, and 0.51, respectively. No patient developed new-onset encapsulating peritoneal sclerosis for at least 1.5 years after peritoneal effuent sampling.CONCLUSION： The effuent MMP-2 level most closely reflected peritoneal solute transport rate. MMP-2 can be a reliable indicator of peritoneal deterioration with functional decline.

Research on Tourism Competitiveness of Chinese Island Counties:Based on Factor and Cluster Analysis

From the perspective of tourism competitiveness,the paper takes 12 island counties of China as the research object,and applies the method of factor analysis to study their competitiveness.The result shows that Putuo and Dinghai are more competitive while Pingtan and Nan'ao are less competitive.Finally,the 12 island counties are divided into four styles:first-class competitive county (Putuo),seond-class competitive counties (Dinghai,Yuhuan),third-class competitive counties (Chongming,Daishan,Changdao,Changhai and Shengsi),fourth-class competitive counties (Dongshan,Dongtou,Pingtan and Nan'ao) by cluster analysis.The classification of island counties is to clear their relative position,then to promote their development.

Vari-gram language model based on word clustering

Category-based statistic language model is an important method to solve the problem of sparse data.But there are two bottlenecks:1) The problem of word clustering.It is hard to find a suitable clustering method with good performance and less computation.2) Class-based method always loses the prediction ability to adapt the text in different domains.In order to solve above problems,a definition of word similarity by utilizing mutual information was presented.Based on word similarity,the definition of word set similarity was given.Experiments show that word clustering algorithm based on similarity is better than conventional greedy clustering method in speed and performance,and the perplexity is reduced from 283 to 218.At the same time,an absolute weighted difference method was presented and was used to construct vari-gram language model which has good prediction ability.The perplexity of vari-gram model is reduced from 234.65 to 219.14 on Chinese corpora,and is reduced from 195.56 to 184.25 on English corpora compared with category-based model.

Design and Implementation of Office Management System in Small and Medium Sized Enterprises based on Data Mining Technology

This paper introduces data mining technology in enterprise competitive intelligence system; and then introduced theoretical foundation and main clustering method of cluster analysis. The article emphasis on the FCM algorithm and principle and described implementation steps, and proposed the improvement FCM algorithm based on K mean particle size; finally, realize the design and implementation of enterprise competitive intelligence analysis and mining service system, and the improved FCM algorithm is applied in the system.

Heterogeneous Responses of Chinese Cities' Housing Prices to Monetary Policies

This works examine the responses of housing prices to the monetary policies in various Chinese cities. Thirty-five large and medium sized Chinese cities are classified into six clusters applying the minimum variance clustering method according to the calculated correlation coefficients between the housing price indices of every two cities.Time difference correlation analysis is then employed to quantify the relations between the housing price indices of the six clusters and the monetary policies.It is suggested that the housing prices of various cities evolved at different paces and their responses to the monetary policies are heterogeneous,and local economic features are more important than geographic distances in determining the housing price trends.

Collision-Based Chosen-Message Simple Power Clustering Attack Algorithm

Chosen-message pair Simple Power Analysis (SPA) attacks were proposed by Boer, Yen and Homma, and are attack methods based on searches for collisions of modular multiplication. However, searching for collisions is difficult in real environments. To circumvent this problem, we propose the Simple Power Clustering Attack (SPCA), which can automatically identify the modular multiplication collision. The insignificant effects of collision attacks were validated in an Application Specific Integrated Circuit (ASIC) environment. After treatment with SPCA, the automatic secret key recognition rate increased to 99%.