ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Analysis of the Employment Situation of Non Private Enterprises in Various Regions of China

In the past 30 years, Chinese enterprises have been a hot topic of discussion and concern among the general public in terms of economic and social status, ownership structure, business mechanism, and management level. Solving the problem of employment for the people is an important prerequisite for their peaceful living and work, as well as a prerequisite and foundation for building a harmonious society. The employment situation of private enterprises has always been of great concern to the outside world, and these two major jobs have always occupied an important position in the employment field of China that cannot be ignored. With the establishment of the market economy system, individual and private enterprises have become important components of the socialist economy, making significant contributions to economic development and social progress. The rapid development of China’s economy, on the one hand, is the embodiment of the superiority of China’s socialist market economic system, and on the other hand, it is the role of the tertiary industry and private enterprises in promoting the national economy. Since the 1990s, China’s private enterprises have become a new economic growth point for local and even national countries, and are one of the important ways to arrange employment and achieve social stability. This paper studies the employment of private enterprises and individuals from the perspective of statistics, extracts relevant data from China statistical Yearbook, uses the relevant knowledge of statistics to process the data, obtains the conclusion and puts forward relevant constructive suggestions.

Slope deformation partitioning and monitoring points optimization based on cluster analysis

The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope.There are still some defects in the layout of monitoring points.To this end,based on displacement data series and spatial location information of surface displacement monitoring points,by combining displacement series correlation and spatial distance influence factors,a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points,based on which the deformation area of the slope was divided.The redundant monitoring points in each partition were eliminated based on the partition's outcome,and the overall optimal arrangement of slope monitoring points was then achieved.This method scientifically addresses the issues of slope deformation zoning and data gathering overlap.It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring.In order to verify the effectiveness of the method,a sand-mudstone interbedded CounterTilt excavation slope in the Chongqing city of China was used as the research object.Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months.The spatial location of the monitoring points was discussed.The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.

Fuzzy cluster analysis of water mass in the western Taiwan Strait in spring 2019

The classification of the springtime water mass has an important influence on the hydrography,regional climate change and fishery in the Taiwan Strait.Based on 58 stations of CTD profiling data collected in the western and southwestern Taiwan Strait during the spring cruise of 2019,we analyze the spatial distributions of temperature(T)and salinity(S)in the investigation area.Then by using the fuzzy cluster method combined with the T-S similarity number,we classify the investigation area into 5 water masses:the Minzhe Coastal Water(MZCW),the Taiwan Strait Mixed Water(TSMW),the South China Sea Surface Water(SCSSW),the South China Sea Subsurface Water(SCSUW)and the Kuroshio Branch Water(KBW).The MZCW appears in the near surface layer along the western coast of Taiwan Strait,showing low-salinity(<32.0)tongues near the Minjiang River Estuary and the Xiamen Bay mouth.The TSMW covers most upper layer of the investigation area.The SCSSW is mainly distributed in the upper layer of the southwestern Taiwan Strait,beneath which is the SCSUW.The KBW is a high temperature(core value of 26.36℃)and high salinity(core value of 34.62)water mass located southeast of the Taiwan Bank and partially in the central Taiwan Strait.

Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means

In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.

CPSO: Chaotic Particle Swarm Optimization for Cluster Analysis

Background:To solve the cluster analysis better,we propose a new method based on the chaotic particle swarm optimization(CPSO)algorithm.Methods:In order to enhance the performance in clustering,we propose a novel method based on CPSO.We first evaluate the clustering performance of this model using the variance ratio criterion(VRC)as the evaluation metric.The effectiveness of the CPSO algorithm is compared with that of the traditional particle swarm optimization(PSO)algorithm.The CPSO aims to improve the VRC value while avoiding local optimal solutions.The simulated dataset is set at three levels of overlapping:non-overlapping,partial overlapping,and severe overlapping.Finally,we compare CPSO with two other methods.Results:By observing the comparative results,our proposed CPSO method performs outstandingly.In the conditions of non-overlapping,partial overlapping,and severe overlapping,our method has the best VRC values of 1683.2,620.5,and 275.6,respectively.The mean VRC values in these three cases are 1683.2,617.8,and 222.6.Conclusion:The CPSO performed better than other methods for cluster analysis problems.CPSO is effective for cluster analysis.

Mapping theme trends and knowledge structures for human neural stem cells:a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

Research status and hotspots of economic evaluation in nursing by co-word clustering analysis

Objective:The aim of this study is to discover research status and hotspots of economic evaluation(EE)in nursing area using co-word cluster analysis.Methods:Medical Subject Heading(MeSH)term“cost–benefit analysis”was searched in PubMed and nursing journals were limited by the function of filter.The information of author,country,year,journal,and keywords of collected paper was extracted and exported to Bicomb 2.0 system,where high-frequency terms and other data could be further mined.SPSS 19.0 was used for cluster analysis to generate dendrogram.Results:In all,3,020 articles were found and 10,573 MeSH terms were detected;among them,1,909 were MeSH major topics and generated 42 high-frequency terms.The consequence of dendrogram showed seven clusters,representing seven research hotspots:skin administration,infection prevention,education program,nurse education and management,EE research,neoplasm patient,and extension of nurse function.Conclusions:Nursing EE research involved multiple aspects in nursing area,which is an important indicator for decision-making.Although the number of papers is increasing,the quality of study is not promising.Therefore,further study may be required to detect nurses’knowledge of economic analysis method and their attitude to apply it into nursing research.More nursing economics course could carry out in nursing school or hospitals.

Co-word clustering analysis for nursing safety management research focuses by PubMed

目的：基于Pubmed数据库分析护理安全管理研究的现状及热点。方法：以“safety management”为主题词，检索2007年9月至2017年9月PubMed数据库中有关护理安全管理的文献，并使用Bicomb软件、SPSS20．0对主题词进行共词聚类分析。结果：共获得文献2353篇，提取高频主题词19个，占总频次的27．50％，通过对高频主题词词篇矩阵进行共词聚类，得到5个研究热点：护理安全文化的研究、团队协作促进护理安全、护理安全管理实践、护理人员工作场所暴力以及护理安全质量评价标准的相关研究。结论：对近10年护理安全管理研究热点的分析有助于了解该领域研究重点及发展趋势，为随后护理安全管理研究及实践提供参考。

Analysis of CLARANS Algorithm for Weather Data Based on Spark

With the rapid development of technology,processing the explosive growth of meteorological data on traditional standalone computing has become increasingly time-consuming,which cannot meet the demands of scientific research and business.Therefore,this paper proposes the implementation of the parallel Clustering Large Application based upon RANdomized Search(CLARANS)clustering algorithm on the Spark cloud computing platformto cluster China’s climate regions usingmeteorological data from1988 to 2018.The aim is to address the challenge of applying clustering algorithms to large datasets.In this paper,the morphological similarity distance is adopted as the similarity measurement standard instead of Euclidean distance,which improves clustering accuracy.Furthermore,the issue of local optima caused by an improper selection of initial clustering centers is addressed by utilizing the max-distance criterion.Compared to the k-means clustering algorithm already implemented in the Spark platform,the proposed algorithm has strong robustness,can reduce the interference of outliers in the dataset on clustering results,and has higher parallel performance than the frequently used serial algorithms,thus improving the efficiency of big data analysis.This experiment compares the clustered centroid data with the annual average meteorological data of representative cities in the five typical meteorological regions that exist in China,and the results show that the clustering results are in good agreement with the meteorological data obtained from the National Meteorological Science Data Center.This algorithm has a positive effect on the clustering analysis of massive meteorological data and deserves attention in scientific research activities.

Group decision-making method based on entropy and experts cluster analysis

According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferences and the consistency of expert＇s collating vectors,but they lack of the measure of information similarity.So it may occur that although the collating vector is similar to the group consensus,information uncertainty is great of a certain expert.However,it is clustered to a larger group and given a high weight.For this,a new aggregation method based on entropy and cluster analysis in group decision-making process is provided,in which the collating vectors are classified with information similarity coefficient,and the experts＇ weights are determined according to the result of classification,the entropy of collating vectors and the judgment matrix consistency.Finally,a numerical example shows that the method is feasible and effective.

Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.

Cluster analysis on summer precipitation field over Qinghai-Tibet Plateau from 1961 to 2004

The summer day-by-day precipitation data of 97 meteorological stations on the Qinghai-Tibet Plateau from 1961 to 2004 were selected to analyze the temporal-spatial distribution through accumulated variance,correlation analysis,regression analysis,empirical orthogonal function,power spectrum function and spatial analysis tools of GIS.The result showed that summer precipitation occupied a relatively high proportion in the area with less annual precipitation on the Plateau and the correlation between summer precipitation and annual precipitation was strong.The altitude of these stations and summer precipitation tendency presented stronger positive correlation below 2000 m,with correlation value up to 0.604（α=0.01）.The subtracting tendency values between 1961-1983 and 1984-2004 at five altitude ranges（2000-2500 m,2500-3000 m,3500-4000 m,4000-4500 m and above 4500 m）were above zero and accounted for 71.4%of the total.Using empirical orthogonal function, summer precipitation could be roughly divided into three precipitation pattern fields：the Southeast Plateau Pattern Field,the Northeast Plateau Pattern field and the Three Rivers＇ Headstream Regions Pattern Field.The former two ones had a reverse value from the north to the south and opposite line was along 35°N.The potential cycles of the three pattern fields were 5.33a,21.33a and 2.17a respectively,tested by the confidence probability of 90%.The station altitudes and summer precipitation potential cycles presented strong negative correlation in the stations above 4500 m,with correlation value of-0.626（α=0.01）.In Three Rivers Headstream Regions summer precipitation cycle decreased as the altitude rose in the stations above 3500 m and increased as the altitude rose in those below 3500 m.The empirical orthogonal function analysis in June precipitation,July precipitation and August precipitation showed that the June precipitation pattern field was similar to the July＇s,in which southern Plateau was positive and northern Plateau negative.But positive value area in July precipitation pattern field was obviously less than June＇s.The August pattern field was totally opposite to June＇s and July＇s.The positive area in August pattern field jumped from the southern Plateau to the northern Plateau.

Optimization of constitutive parameters of foundation soils k-means clustering analysis

The goal of this study was to optimize the constitutive parameters of foundation soils using a k-means algorithm with clustering analysis. A database was collected from unconfined compression tests, Proctor tests and grain distribution tests of soils taken from three different types of foundation pits： raft foundations, partial raft foundations and strip foundations. k-means algorithm with clustering analysis was applied to determine the most appropriate foundation type given the un- confined compression strengths and other parameters of the different soils.

Bohai crude oil identification by gas chromatogram fingerprinting quantitative analysis coupled with cluster analysis

By gas chromatogram, six crude oils fingerprinting distributed in four oilfields and four oil platforms were analyzed and the corre- sponding normal paraffin hydrocarbon （ including pristane and phytane） concentration was obtained by the internal standard methed. The normal paraffin hydrocarbon distribution patterns of six crude oils were built and compared. The cluster analysis on the normal paraffin hydrocarbon concentration was conducted for classification and some ratios of oils were used for oils comparison. The results indicated： there was a clear difference within different crude oils in different oil fields and a small difference between the crude oils in the same oil platform. The normal paraffin hydrocarbon distribution pattern and ratios, as well as the cluster analysis on the nomad paraffin hydrocarbon concentration can have a better differentiation result for the crude oils with small difference than the original gas chromatogram.

Establishment of HPLC Fingerprint, Cluster Analysis and Principle Component Analysis of Citri Reticulatae Pericarpium Viride

[Objectives] This study aimed to establish HPLC fingerprint and conduct cluster analysis and principle component analysis for Citri Reticulatae Pericarpium Viride. [Methods] Using the HPLC method, the determination was performed on XSelect~&#x00AE; HSS T3-C_(18) column with mobile phase of acetonitrile-0.5% acetic acid solution(gradient elution) at the flow rate of 1.0 mL/min. The detection wavelength was 360 nm. The column temperature was 25℃. The sample size was 10 μL. With peak of hesperidin as the reference, HPLC fingerprints of 10 batches of Citri Reticulatae Pericarpium Viride were determined. The similarity of the 10 batches of samples was evaluated by Similarity Evaluation System for Chromatographic Fingerprint of TCM(2012 edition) to determine the common peaks. Cluster analysis and principal component analysis were performed by using SPSS 17.0 statistical software. [Results] The HPLC fingerprints of the 10 batches of medicinal materials had total 11 common peaks, and the similarity was 0.919-1.000, indicating that the chemical composition of the 10 batches of medicinal materials was consistent. There were 11 common components in the 10 batches of medicinal materials, but their contents were different. When the Euclidean distance was 20, the 10 batches of samples were divided into two categories, S4 in the first category, and the others in the second one. When the Euclidean distance was 5, the second category could be further divided into two sub-categories, S1 and S10 in one sub-category, and S2, S3, S5, S6, S7, S8 and S9 in the other one. The principle component analysis showed that cumulative contribution rate of the two main component factors was 92.797%, and the comprehensive score of S7 was the highest with the best quality. [Conclusions] The results of HPLC fingerprinting, cluster analysis and principle component analysis can provide reference for the quality control of Citri Reticulatae Pericarpium Viride.

Cluster Analysis on Japonica Rice(Oryza sativa L.) with Good Eating Quality Based on SSR Markers and Phenotypic Traits

Diversity of 60 conventional japonica rice accessions with good eating quality at home and abroad was analyzed using SSR molecular markers, agronomic traits and taste characteristics. A total of 290 alleles were detected in the 60 accessions at 72 SSR loci with the high similarity coefficients varying between 0.600 and 0.924. The loci on chromosome 5 showed the greatest value in average allele number. Additionally, most of the SSR loci could detect 3 to 4 alleles. An UPGMA dendrogram based on the cluster analysis of the genetic similarity coefficients showed that the grouping trend of part of the rice accessions was geographic-related and most of the rice accessions in Jiangsu Province, China were clustered together. Furthermore, many domestic accessions from south and north origins in China were close to the foreign japonica rice varieties, as proved by their pedigree origin from the foreign high-quality sources. For taste characteristics, part of the accessions with excellent taste were clearly clustered into one category though they came from different geographical regions, which indicates that taste characteristics of some varieties were mainly genetically determined. In addition, the agronomic traits of japonica rice with good taste might be closely related with their geographical origins, but the relationship between superior taste characteristics and agronomic traits should be further clarified.

Fruit and Vegetable Nutrition Value Assessment and Replacement Based on the Principal Component Analysis and Cluster Analysis

Utilizing principal component analysis (PCA) and cluster analysis, the standardization, dimension-reduction and de-correlation of multiple evaluation index system for fruit and vegetable nutrition are performed to assign principal component factor based on cluster analysis of loading matrix and combining with actual meaning and evaluation direction of index categories. To evaluate the richness of its nutrition according to the score of nutrition of fruit and vegetable, finally equivalent replacement suggestions are given in different seasons of vegetables & fruits according to the result of clustering. Studies show that principal component cluster method can not only carry on the reasonable classification of multivariate data effectively, but also make reasonable evaluation on the sample object, and provide powerful basis for evaluation of fruits and vegetables’ nutrition.

Vertical Migrating and Cluster Analysis of Soil Mesofauna at Dongying Halophytes Garden in Yellow River Delta

For the first time, we used Tullgren method made a study on vertical migrating and cluster analysis of the soil mesofauna in Dongying Halophytes Garden in the Yellow River Delta （YRD）, Shandong Province. The results showed that the soil mesofauna tended to gather on soil surface in most samples at most times, but the vertical migrating greatly varied in different seasons or environment conditions. Acari was the dominant group. The index of diversity of the soil fauna was correlated with the index of evenness. The Acari＇s number of individuals infected other species and numbers. Dominant group-Aeari made greater contribution to the result of cluster analysis, and there were significant differences between communities in different habitats by cluster analysis with both Bray-Curtis and Jaccard similarity coefficient.