Comparative Analysis of Differences among Northern,Jiangnan,and Lingnan Classical Private Gardens Using Principal Component Cluster Method

This paper investigates the design essence of Chinese classical private gardens,integrating their design elements and fundamental principles.It systematically analyzes the unique characteristics and differences among classical private gardens in the Northern,Jiangnan,and Lingnan regions.The study examines nine classical private gardens from Northern China,Jiangnan,and Lingnan by utilizing the advanced tool of principal component cluster analysis.Based on literature analysis and field research,273 variables were selected for principal component analysis,from which four components with higher contribution rates were chosen for further study.Subsequently,we employed clustering analysis techniques to compare the differences among the three types of gardens.The results reveal that the first principal component effectively highlights the differences between Jiangnan and Lingnan private gardens.The second principal component serves as the key to defining the types of Northern private gardens and distinguishing them from the other two types,and the third principal component indicates that Lingnan private gardens can be categorized into two distinct types as well.

Study on Trace Elements in Rehmannia glutinosa Libosch. by Principal Component Analysis and Clustering Analysis

[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering analysis of R. glutinosa medicinal materials from different sources were conducted with contents of six trace elements as indices. [Result] The principal component analysis could comprehen- sively evaluate the quality of R. glutinosa samples with objective results which was consistent with the results of clustering analysis. [Conclusion] Principal component analysis and clustering analysis methods can be used for the quality evaluation of Chinese medicinal materials with multiple indices.

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.

Relationship of public preferences and behavior in residential outdoor spaces using analytic hierarchy process and principal component analysis—a case study of Hangzhou City, China

This study examined public attitudes concerning the value of outdoor spaces which people use daily. Two successive analyses were performed based on data from common residents and college students in the city of Hangzhou, China. First, citizens registered various items constituting desirable values of residential outdoor spaces through a preliminary questionnaire. The result proposed three general attributes (functional, aesthetic and ecological) and ten specific qualities of residential outdoor spaces. An analytic hierarchy process (AHP) was applied to an interview survey in order to clarify the weights among these attributes and qualities. Second, principal factors were extracted from the ten specific qualities with principal component analysis (PCA) for both the common case and the campus case. In addition, the variations of respondents’ groups were classified with cluster analysis (CA) using the results of the PCA. The results of the AHP application found that the public prefers the functional attribute, rather than the aesthetic attribute. The latter is always viewed as the core value of open spaces in the eyes of architects and designers. Fur-thermore, comparisons of ten specific qualities showed that the public prefers the open spaces that can be utilized conveniently and easily for group activities, because such spaces keep an active lifestyle of neighborhood communication, which is also seen to protect human-regarding residential environments. Moreover, different groups of respondents diverge largely in terms of gender, age, behavior and preference.

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.

Application of Surface Water Quality Classification Models Using Principal Components Analysis and Cluster Analysis

Water quality monitoring has one of the highest priorities in surface water protection policy. Many variety approaches are being used to interpret and analyze the concealed variables that determine the variance of observed water quality of various source points. A considerable proportion of these approaches are mainly based on statistical methods, multivariate statistical techniques in particular. In the present study, the use of multivariate techniques is required to reduce the large variables number of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs) and determination of relationships among them for easy and robust evaluation. By means of multivariate statistics of principal components analysis (PCA), Fuzzy C-Means (FCM) and K-means algorithm for clustering analysis, this study attempted to determine the major dominant factors responsible for the variations of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs). Furthermore, cluster analysis classified 21 sampling stations into three clusters based on similarities of water quality features. The result of PCA shows that 6 principal components contain the key variables and account for 75.82% of total variance of the study area surface water quality and the dominant water quality parameters were: Conductivity, Iron, Biological Oxygen Demand (BOD), Total Coliform (TC), Ammonia (NH3), and pH. However, the results from both of FCM clustering and K-means algorithm, based on the dominant parameters concentrations, determined 3 cluster groups and produced cluster centers (prototypes). Based on clustering classification, a noted water quality deteriorating as the cluster number increased from 1 to 3. However the cluster grouping can be used to identify the physical, chemical and biological processes creating the variations in the water quality parameters. This study revealed that multivariate analysis techniques, as the extracted water quality dominant parameters and clustered information can be used in reducing the number of sampling parameters on the Nile River in a cost effective and efficient way instead of using a large set of parameters without missing much information. These techniques can be helpful for decision makers to obtain a global view on the water quality in any surface water or other water bodies when analyzing large data sets especially without a priori knowledge about relationships between them.

Principal Component Analysis and Cluster Analysis of Luffa Germplasm Resources in Zhejiang Province

In this study, 32 Luffa germplasm resources were collected from various regions in Zhejiang Province as experimental materials, to investigate 22 agronomic traits including fruit bearing habit, leaf margin, fruit ribbing and percentage of nodes with female flowers to total node. Based on the obtained experimental data, principal component analysis and cluster analysis were carried out using DPS software. The results showed that 22 agronomic traits could be integrated into 5 principal components, with the cumulative contributive percentage of 81. 308%. According to the correlations between the first five principal components and traits, 14 traits with great influences were screened. On the basis of principal component analysis, cluster analysis of 32 Luffa germplasm resources was conducted, which divided Luffa cylindrica and Luffa acutangula into two categories and six subcategories by Euclidean genetic distances. This study provided scientific basis for the collection, preservation, identification, creation and utilization of Luffa germplasm and parent selection in cross breeding of Luffa.

Description and Classification of Leather Defects Based on Principal Component Analysis

The accurate extraction and classification of leather defects is an important guarantee for the automation and quality evaluation of leather industry. Aiming at the problem of data classification of leather defects,a hierarchical classification for defects is proposed.Firstly,samples are collected according to the method of minimum rectangle,and defects are extracted by image processing method.According to the geometric features of representation, they are divided into dot,line and surface for rough classification. From analysing the data which extracting the defects of geometry,gray and texture,the dominating characteristics can be acquired. Each type of defect by choosing different and representative characteristics,reducing the dimension of the data,and through these characteristics of clustering to achieve convergence effectively,realize extracted accurately,and digitized the defect characteristics,eventually establish the database. The results showthat this method can achieve more than 90% accuracy and greatly improve the accuracy of classification.

Principal component analysis and cluster analysis based orbit optimization for earth observation satellites

This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverage time, the frequency of coverage, average time per coverage and maximum coverage gap, etc. is required simultaneously. By introducing index normalization method to convert performance indices into dimensionless variables within the range of [0, 1], a design optimization method based on the principal component analysis and cluster analysis is proposed, which consists of index normalization method, principal component analysis, multiple-level cluster analysis and weighted evaluation method. The results of orbit optimization for earth observation satellites show that the optimal orbit can be obtained by using the proposed method. The principal component analysis can reduce the total number of indices with a non-independent relationship to save computing time. Similarly, the multiple-level cluster analysis with parallel computing could save computing time.

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.

Application of Principal Component Analysis, Cluster Analysis, Pollution Index and Geoaccumulation Index in Pollution Assessment with Heavy Metals from Gold Mining Operations, Tanzania

Gold mining is now widely acknowledged as one of the significant sources of soil pollution in developed countries. In developing countries, the sources and levels of soil contamination have not been thoroughly addressed. Thus, this study was intended to determine the source of soil pollution and the level of contamination in the active and closed gold mining areas. The research paper presents the pollution load of heavy metals (lead-Pb, chromium-Cr, cadmium-Cd, copper-Cu, arsenic-As, manganese-Mn, and nickel-Ni) in 90 soil samples collected from the studied sites. Multivariate statistical analysis, including Principal Component Analysis (PCA) and Cluster Analysis (CA), coupled with correlation coefficient analysis, was performed to determine the possible sources of pollution in the study areas. The results indicated that Pb, Cr, Cu and Mn come from different sources than Cd, As and Ni. The results obtained from the metal pollution assessment using the Pollution Index (PI) and the Geoaccumulation Index (Igeo) confirmed that soils in the mining areas were contaminated in the range from moderately through strongly to highly contaminated soils. This study verified that soil contamination in the gold mining areas results from natural and anthropogenic processes. The current study findings would enhance our knowledge regarding the soil contamination level in the mining areas and the source of contamination. It is recommended to use PCA, CA, PI and Igeo to assess and monitor the heavy metal contaminated soil in gold mining areas.

Comprehensive Evaluation of the Economic Development Level of Guanzhong-Tianshui Economic Zone Using Principal Component Cluster Analysis

Based on 10 years of statistics concerning economic development in Xi'an as the main part of Guanzhong- Tianshui Economic Zone, this article builds the main indicator system to reflect economic development. Using two mathematical methods( principal component analysis and cluster analysis),we carry out comprehensive evaluation analysis of the main economic indicators,point out the distribution differences in the economic development level in this region,and make classification,in order to provide a scientific basis for the decision- making body to lay down the relevant economic development strategies in accordance with the economic development level and geographical location.

Determination of bioactive components in the fruits of Cercis chinensis Bunge by HPLC-MS/MS and quality evaluation by principal components and hierarchical cluster analyses

The fruits of leguminous plants Cercis Chinensis Bunge are still overlooked although they have been reported to be antioxidative because of the limited information on the phytochemicals of C.chinensis fruits.A simple,rapid and sensitive HPLC-MS/MS method was developed for the identification and quantitation of the major bioactive components in C.chinensis fruits.Eighteen polyphenols were identified,which are first reported in C.chinensis fruits.Moreover,ten components were simultaneously quantified.The validated quantitative method was proved to be sensitive,reproducible and accurate.Then,it was applied to analyze batches of C.chinensis fruits from different phytomorph and areas.The principal components analysis(PCA)realized visualization and reduction of data set dimension while the hierarchical cluster analysis(HCA)indicated that the content of phenolic acids or all ten components might be used to differentiate C.chinensis fruits of different phytomorph.

Analysis of Differences in Biochemical Components Between Yunnan and Kenya Tea Tree Varieties

With 16 Yunnan tea tree varieties and 5 Kenya tea tree varieties as test materials,the differences in biochemical components between Yunnan and Kenya tea tree varieties were compared and analyzed.The results showed that the coefficients of variation of tea polyphenols,amino acids,caffeine,water extract,gallic acid(GA),catechin(C),epicatechin(EC),epicatechin gallate(ECG),epigallocatechin(EGC),epigallocatechin gallate(EGCG)and total catechins in Yunnan tea tree varieties were greater than those in Kenyan tea trees.The contents of tea polyphenols,amino acids,caffeine,water extract,C,EC,EGC,EGCG and total catechins in Yunnan tea tree varieties had no significant differences from those in Kenyan tea trees varieties(P>0.05),while there were significant differences in the contents of GA and ECG between Yunnan tea tree varieties and Kenya tea tree varieties(P<0.05).Therefore,it could be predicted that GA and ECG might be one of the main characteristics of the differences in biochemical components between Yunnan tea tree varieties and Kenyan tea tree varieties.The cluster analysis results showed that when the genetic distance was 15,the 21 tested tea varieties could be divided into three groups with obvious biochemical differences.

Comparison of Kernel Entropy Component Analysis with Several Dimensionality Reduction Methods

Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing.

Principal Component-Discrimination Model and Its Application

Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.

Water Quality Evaluation of Chapurson Valley in Hunza Nagar, Gilgit Baltistan, Pakistan, Based on Statistical Analysis and Water Quality Index

Water borne ailments are of serious public health concern in Gilgit Baltistan’s (GB) region of Pakistan. The pollution load on the glacio-fluvial streams and surface water resources of the Chapurson Valley in the Hunza Nagar area of the GB is increasing as a result of anthropogenic activities and tourism. The present study focuses on the public health quality of drinking water of Chapurson valley. The study addressed the fundamental drinking water quality criteria in order to understand the state of the public health in the valley. To ascertain the current status of physico-chemical, metals, and bacteriological parameters, 25 water samples were collected through deterministic sampling strategy and examined accordingly. The physico-chemical parameters of the water samples collected from the valley were found to meet the World Health Organization (WHO) guidelines of drinking water. The water samples showed a pattern of mean metal concentrations in order of Arsenic (As) > Lead (Pb) > Iron (Fe) > Zinc (Zn) > Copper (Cu) > Magnesium (Mg) > Calcium (Ca). As, Cu, Zn, Ca and Mg concentration were under the WHO guidelines range. However, results showed that Pb and Fe are present at much higher concentrations than recommended WHO guidelines. Similarly, the results of the bacteriological analysis indicate that the water samples are heavily contaminated with the organisms of public health importance (including total coliforms (TCC), total faecal coliforms (TFC) and total fecal streptococci (TFS) are more than 3 MPN/100mL). Three principal components, accounting for 48.44% of the total variance, were revealed using principal component analysis (PCA). Bacteriological parameters were shown to be the main determinants of the water quality as depicted by the PCA analysis. The dendrogram of Cluster analysis using the Ward’s method validated the same traits of the sampling locations that were found to be contaminated during geospatial analysis using the Inverse Distance Weight (IDW) method. Based on these findings, it is most likely that those anthropogenic activities and essentially the tourism results in pollution load from upstream channels. Metals may be released into surface and groundwater from a few underlying sources as a result of weathering and erosion. This study suggests that the valley water resources are more susceptible to bacteriological contamination and as such no water treatment facilities or protective measure have been taken to encounter the pollution load. People are drinking the contaminated water without questioning about the quality. It is recommended that the water resources of the valley should be monitored using standard protocol so as to protect not only the public health but to safe guard sustainable tourism in the valley.

云南不同品种核桃果实品质分析与综合评价

为了探究云南主栽核桃和薄壳山核桃果实的品质评价指标,选取27个核桃品种果实,对矿质元素、蛋白脂、粗脂肪等12个营养指标进行变异和相关性分析,并采用主成分和聚类分析方法,进行分类与综合评价。结果表明:12个营养指标的含量变异较为丰富,变异系数的变化范围为5%~83%,其中VC变异系数最大,粗脂肪变异系数最小;9种矿质元素平均含量从高到低依次为:K>P>Mg>Ca>Mn>Na>Fe>Zn>Cu,蛋白质含量均值为15.62%,脂肪含量均值为68.63%,VC的平均含量为4.66 mg/100 g;相关性分析表明,12个营养指标之间,P、Fe、Mg、Ca、Cu均与蛋白质呈极显著(P<0.01)正相关;主成分分析进行综合评价筛选出品质最佳的核桃品种为‘香茶’‘永平’‘晚熟’和‘小圆果’,薄壳山核桃中品种‘卡多’综合表现较好;聚类分析结果显示,可将其分为两大类,即富含矿质元素类群和高脂肪含量类群;相关性分析和主成分分析结果显示,K、Fe、Mg、Ca、Cu元素、蛋白质、脂肪为评价云南不同品种核桃品质的关键性指标。薄壳山核桃适合榨油,22种主栽核桃适合直接食用或开发富矿质元素和高蛋白的功能饮料。研究明确了云南核桃品质评价的关键指标并探明品质特性,提供科学分类方法,为云南核桃品质评价体系的构建奠定理论依据。

不同前处理工艺红桔果汁与红桔果酒挥发性风味物质分析

以万州红桔为研究对象,采用带皮和去皮方式榨汁,对浊汁进行酶解澄清处理,分别利用带皮浊汁、带皮清汁、去皮浊汁及去皮清汁酿造红桔果酒。对果酒进行感官评价,同时利用液相色相仪与顶空固相微萃取(HS-SPME)-气相色谱质谱(GC-MS)联用技术测定果酒和果汁样品中有机酸及挥发性风味物质,并基于挥发性风味物质进行主成分分析(PCA)和聚类分析(CA)。结果表明,红桔果汁主要有机酸为苹果酸和柠檬酸,红桔果酒增加了乳酸和乙酸;红桔果汁和红桔果酒中共检测出100种挥发性风味物质,主要化合物包括酯类28种、醇类19种、酚类3种、醚类2种、醛类8种、酮类10种、酸类4种、烷烃类3种及烯烃类23种;总体上,清汁发酵红桔果酒的挥发性风味物质种类较浊汁发酵多,带皮发酵果酒中的醇类和烯烃种类较去皮发酵多。PCA结果表明,不同红桔果汁和红桔果酒样品中主要挥发性风味物质为辛酸乙酯、癸酸乙酯、正己酸乙酯、月桂酸乙酯、棕榈酸乙酯、α-松油醇、苯乙醇、辛醇、香叶醇、麝香草酚、L-紫苏醛、癸醛等;聚类分析结果表明,可将样品聚集为三大类,与PCA分类结果一致。