Applied research of correspondence analysis method in waste tailings reservoir heavy metal pollution monitoring points optimization

In order to optimize monitoring points and monitoring factor, the relationshipbetween pollutants and soil sample were established by correspondence analysis.Thestudy results show that the reflecting monitoring points and monitoring factors in thegraphic on the same factor axis can clearly express the intrinsic link between pollutantsand monitoring points and distribution characteristics.To determine the main monitoringpoint and the main monitoring indicators can reduce and optimize the number of monitoringpoints under the premise of ensuring the typical and representative of monitoring data.Using the correlation of pollutants can reduce the number of monitoring indicators and improvethe effectiveness of data collection.

Visualization of Special Features in “The Tale of Genji” by Text Mining and Correspondence Analysis with Clustering

In this paper, visualization of special features in “The Tale of Genji”, which is a typical Japanese classical literature, is studied by text mining the auxiliary verbs and examining the similarity in the sentence style by the correspondence analysis with clustering. The result shows that the text mining error in the number of auxiliary verbs can be as small as 15%. The extracted feature in this study supports the multiple authors of “The Tale of Genji”, which agrees well with the result by Murakami and Imanishi [1]. It is also found that extracted features are robust to the text mining error, which suggests that the classification error is less affected by the text mining error and the possible use of this technique for further statistical study in classical literatures.

Screening of Long Cowpea[Vigna unguiculata(L.)Walp.ssp.sesquipedialis]Varieties in Autumn in Hunan and Comparison of Various Comprehensive Evaluation Methods

[Objectives]The paper was to screen new varieties of long cowpea that are suitable for autumn cultivation in Hunan,as well as to develop a comprehensive evaluation method to assess their adaptability and performance.[Methods]A total of 48 long cowpea varieties were introduced,and a range of comprehensive evaluation methods was employed to assess these varieties through the collection and analysis of field data.[Results]The square Euclidean distance of 14 allowed for the classification of all varieties into eight distinct groups.Groups II,III,and V belong to the autumn dominant group within this region,while groups I and VIII belong to the intermediate group.Additionally,groups IV,VI,and VII belong to the autumn inferior group in this area.Through a comparative analysis of various comprehensive evaluation methods,it was determined that the common factor comprehensive evaluation,grey correlation method,and fuzzy evaluation method were appropriate for application in the selection of long cowpea varieties.Furthermore,the evaluation outcomes were largely consistent with the cluster pedigree diagram.[Conclusions]Through comprehensive index method,ten varieties demonstrating superior performance in autumn cultivation have been identified,including C20,C42,C29,C40,C3,C14,C18,C25,C15,and C47.The selected varieties exhibit several advantageous traits,such as a reduced growth duration,a lower position of initial flower nodes,a decreased number of branches,predominantly green young pods,elongated pod strips,thicker pod structures,an increased number of pods per plant,and higher overall yields.These characteristics render them particularly valuable for extensive cultivation.

Improvement of ecological geographic regionalization based on remote sensing and canonical correspondence analysis： A case study in China

Ecological geographic regions, also called eco-regions, can be used to divide a remotely sensed image, which is helpful for reducing the complexity of land cover types within eco-regions and for improving the classification accuracy of land cover. In this case study in China, we improved a method of ecological geographic regionalization that is more suitable for remote sensing mapping of regional land cover, and we obtained new eco-regions. The canonical correspondence analysis(CCA) and k-means clustering were adopted in the ecological geographic regionalization using both seasonal remotely-sensed vegetation information and environmental data including climate, elevation and soil features. Our results show that the combination of seasonal vegetation information and the CCA performed well in the selection of the dominant environmental factor of the biogeographic pattern, and it can be used as regionalization indicators of eco-regions. We found that thermal factors are the most important driving forces of the biogeographic pattern in China, which followed by moisture factors. Two global land cover products(MODIS MCD12C1 and Glob Cover) were used to assess our eco-regions. The results show that our eco-regions performed better than that of a previous study regarding the complexity of land cover types, such as in the number of types and the proportional area of the major/secondary type. These results indicate that the method of ecological geographic regionalization, which is based on environmental factors associated with seasonal vegetation features, is effective for reducing the regional complexity of land cover.

Time Slice Analysis Method Based on OTCA Used in fMRI Weak Signal Function Extraction

The original temporal clustering analysis (OTCA) is an effective technique for obtaining brain activation maps when the timing and location of the activation are completely unknown, but its deficiency of sensitivity is exposed in processing brain activation signal which is relatively weak. The time slice analysis method based on OTCA is proposed considering the weakness of the functional magnetic resonance imaging (fMRI) signal of the rat model. By dividing the stimulation period into several time slices and analyzing each slice to detect the activated pixels respectively after the background removal, the sensitivity is significantly improved. The inhibitory response in the hypothalamus after glucose loading is detected successfully with this method in the experiment on rat. Combined with the OTCA method, the time slice analysis method based on OTCA is effective on detecting when, where and which type of response will happen after stimulation, even if the fMRI signal is weak.

A Research on Competitiveness of Guangxi City——Based on System Clustering Method and Principal Component Analysis Method

A total of 10 indices of regional economic development in Guangxi are selected.According to the relevant economic data,regional economic development in Guangxi is analyzed by using System Clustering Method and Principal Component Analysis Method.Result shows that System Clustering Method and Principal Component Analysis Method have revealed similar results analysis of economic development level.Overall economic strength of Guangxi is weak and Nanning has relatively high scores of factors due to its advantage of the political,economic and cultural center.Comprehensive scores of other regions are all lower than 1,which has big gap with the development of Nanning.Overall development strategy points out that Guangxi should accelerate the construction of the Ring Northern Bay Economic Zone,create a strong logistics system having strategic significance to national development,use the unique location advantage and rely on the modern transportation system to establish a logistics center and business center connecting the hinterland and the Asean Market.Based on the problems of unbalanced regional economic development in Guangxi,we should speed up the development of service industry in Nanning,construct the circular economy system of industrial city,and accelerate the industrialization process of tourism city in order to realize balanced development of regional economy in Guangxi,China.

A New Approach to Investigate Students’ Behavior by Using Cluster Analysis as an Unsupervised Methodology in the Field of Education

The problem of taking a set of data and separating it into subgroups where the elements of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied through the statistical method of cluster analysis. In this paper we want to discuss the application of this method to the field of education: particularly, we want to present the use of cluster analysis to separate students into groups that can be recognized and characterized by common traits in their answers to a questionnaire, without any prior knowledge of what form those groups would take (unsupervised classification). We start from a detailed study of the data processing needed by cluster analysis. Then two methods commonly used in cluster analysis are before described only from a theoretical point a view and after in the Section 4 through an example of application to data coming from an open-ended questionnaire administered to a sample of university students. In particular we describe and criticize the variables and parameters used to show the results of the cluster analysis methods.

大凉山地区不同马铃薯品种产量和营养品质的综合评价

为筛选出适宜四川大凉山地区种植的马铃薯品种,对14个马铃薯品种的12个产量与营养品质指标进行测定,并结合主成分分析、隶属函数法、系统聚类分析对马铃薯产量和品质表现进行综合评价。结果表明,单株结薯数、单薯鲜质量、单株块茎鲜质量、单产、还原糖含量的变异系数均超过30%;单株块茎鲜质量与单薯鲜质量、单产和蛋白质含量呈极显著正相关,干物质含量与淀粉含量、蛋白质含量呈极显著正相关,单薯鲜质量与VC含量、锌含量呈显著负相关,蛋白质含量与钾含量呈极显著负相关;主成分分析结果表明,12个指标可用4个主成分来表示,方差累积贡献率达到86.040%。进一步采用隶属函数法和系统聚类分析将14个品种分为3类,筛选出6个综合表现较优的品种,分别为川凉薯10号、青薯9号、川芋50、川凉芋13、云薯108、川芋22号。

基于因子分析法与聚类分析法的河南省农产品物流发展水平研究

2023年中央一号文件提出“坚持不懈地把解决好‘三农’问题作为全党工作重中之重,举全党全社会之力全面推进乡村振兴,加快农业农村现代化”,推动农业发展,助力乡村振兴是我国的重点工作。为了研究河南省农产品物流情况,助力农业发展,以河南省的18个省辖市为研究样本,借助SPSS.23软件进行因子分析,以评价各市农产品物流不同的发展水平。利用聚类分析,按照农产品物流发展水平的综合得分将河南省的18个省辖市分为4类,最后有针对性的提出相关建议以推进各地区农产品物流高效发展。

数字经济背景下智慧供应链发展水平测度——以长江经济带为例

物联网、云计算及大数据等技术的深度融合促进了数字经济的发展,也促进了区域智慧供应链的发展。长江经济带是连接东西部的重要区域,其智慧供应链的发展水平是判断其良性发展的重要依据。为此文章基于数字经济背景,从资源、输出和柔性三个维度构建了长江经济带智慧供应链发展水平评价体系,采用博弈论组合赋权法确定各指标权重,测算了长江经济带11省(市)2016年至2021年智慧供应链发展水平,运用系统聚类分析法和马尔科夫链统计分析方法探索了长江经济带智慧供应链的时空特征及未来发展趋势,并根据分析结果对其智慧供应链发展提出相应的建议。

蝴蝶兰种质资源生物学性状综合评价

以收集的57份蝴蝶兰种质资源为研究对象,对其23个生物学性状进行调查并分析,明确影响蝴蝶兰种质资源生物学性状综合评价的主要因子,采用层次分析法与K-Means聚类分析方法相结合,建立蝴蝶兰种质资源生物学性状综合评价体系,以期能够较为全面的评价现有种质,对现有种质资源进行分级,为蝴蝶兰种质资源的利用、新品种选育及栽培推广提供参考依据。结果表明:花部形态(C1)是蝴蝶兰综合性状评价的主要因子,花色所占比重最大,花梗形态(C2)次之,植株形态(C3)权重最低;综合评价得分最高的是‘光芒四射’(得分4.2706);花部形态评价得分最高的是‘招财猫’和‘光芒四射’(得分2.5010),植株形态得分最高的是‘龙树枫叶’(得分0.7472),花梗形态得分最高的是‘1713’(得分1.1554);根据评价得分将57份种质分为4个等级,其中优秀等级9个,占比15.79%;良好18个,占比31.58%;中等20个,占比35.09%;及格10个,占比17.54%。

基于空间投影和聚类划分的SVR加速算法

数据不仅能产生价值,还对统计学的科学发展提供了动力。随着科技的飞速发展,海量数据得以涌现,但大规模的数据会导致很多传统处理方法很难满足各领域对数据分析的需求。面对海量数据时代学习算法的低效性,分治法通常被认为是解决这一问题最直接、最广泛使用的策略。SVR是一种强大的回归算法,在模式识别和数据挖掘等领域有广泛应用。然而在处理大规模数据时,SVR训练效率低。为此,该文利用分治思想提出一种基于空间投影和聚类划分的SVR加速算法(PKM-SVR)。利用投影向量将数据投影到二维空间;利用聚类方法将数据空间划分为k个互不相交的区域;在每个区域上训练SVR模型;利用每个区域的SVR模型预测落入同一区域的待识别样本。在标准数据集上与传统的数据划分方法进行对比实验,实验结果表明该算法训练速度较快,并表现出更好的预测性能。

管制员个体工作负荷多维量化研究

为提高空管系统高效运行,聚焦管制员个体工作负荷建立量化模型;首先设计试验采集一线16名区域管制员的岗前与岗后各项指标数据,根据测试数据变化,选择出敏感变量,描述个体工作负荷;其次建立包含心理感知负荷、生理反应负荷与脑力工作负荷3个维度的综合评估指标体系,构建管制员个体工作负荷指数模型;然后通过熵权-客观组合法求解个体工作负荷指数最优权重,最终得出管制员个体工作负荷量化模型;最后进一步根据管制员个体工作负荷综合指数进行K-Means聚类分析,结果表明:管制员因个体不同岗后工作负荷存在差异。依据个体工作负荷指数大小,管制员可分为3类,A类管制员数量占总人数50%,岗后个体工作负荷增长最小;B类管制员数量占总人数43.75%,岗后负荷增长居中;C类管制员数量占总人数6.25%,岗后负荷增长最大,与教员对管制员能力的评分结果一致。

基于多维标度和聚类分析的“物质结构和性质”模块情境素材分类研究

不同类别情境素材具有不同的教学功能.在“物质结构与性质”模块教学中,合理选用情境素材能促进学生理解与辨析、逻辑与推理等能力的培养.为系统探察“物质结构与性质”模块情境素材的类别,以课程标准和人教版选择性必修2为源,通过文本研究法和问卷调查法筛选出35个情境素材.以中学化学一线教师以及某师范大学本科生、研究生为研究对象,通过系统聚类法确定情境素材的类别.研究发现,“物质结构和性质”模块情境素材分为社会生活、学术探究、科技前沿、模拟联想、科学史实、实验探索6类.研究可为学者和一线教师高效开展情境教学研究与实践提供参考.

基于图卷积神经网络的滑行时间预测研究

为准确预测滑行时间,提出一种基于机场场面运行态势演变的图卷积神经网络预测方法。首先,根据机场场面航空器时空分布情况,从路段流量、路段密度、路段速度等多角度构建交通态势指标体系;其次,利用主成分分析法对指标进行降维处理并利用K-means算法实现对机场场面路段的态势等级划分,绘制机场场面时空分布热力图;最后,利用图卷积神经网络(GCN)结合门控循环单元(GRU)来获取场面路段特征数据的时空特征,将GRU作为解码器预测输出滑行时间。以深圳宝安国际机场AirTOP仿真数据为例,对所提出的方法进行了分析和验证,并获得了符合预期的预测结果。实验结果表明,该方法在预测滑行时间方面具有有效性。

基于AHP方法的应急物流配送网络优化研究

针对传统物流配送方式效率低,配送网络复杂的问题,基于AHP法和聚类分析法对应急物流配送网络进行优化调整,重新规划配送中心选址,最短配送路线采用层次分析法设计。利用SPSS软件进行聚类分析,从而优化特殊时期下应急物资的配送路线,优化后的配送网络力求能用最快的速度响应,从而保障应急物流配送的时效性。

新农科建设研究热点分析

培养知农爱农为农的高素质农业技术人才,是现代农业发展和高等教育变革发展的现实客观需求,而新农科建设面向国家重大战略和社会经济发展,推进高等农业教育的专业优化、教学改革、课程建设和人才培养等,最终达成农业人才培养目的。该文利用文献计量分析工具,对2018年至今的新农科建设国内研究文献进行分析,提取研究热点为相关研究提供参考。

莽山浪畔湖国家重要湿地植物多样性及环境解释

研究莽山高山湖泊湿地植物多样性对环境因子的响应分析,不仅有助于深入了解湿地植物对环境变化下的适应策略,同时对保护当地湿地珍稀濒危物种具有重要价值。采用系统聚类分析、典范对应分析(CCA)和Pearson相关性分析等方法,对研究区30个植物群落样方进行分析,研究植物多样性与环境因子(土壤、水)之间的相互关系。研究结果表明:浪畔湖国家重要湿地植物群落可分为蕨状苔草+宽叶泽苔草群丛、雪白睡莲+莼菜群丛、水毛花+宽叶泽苔草群丛、莼菜+宽叶泽苔草群丛等4类。雪白睡莲+莼菜群丛Patrick丰富度指数和Pielou均匀度指数最低,Simpson多样性指数最高,水毛花+宽叶泽苔草群丛与之相反。浪畔湖国家重要湿地植物多样性与土壤速效钾和水深呈极显著相关性,与水质氨氮呈显著相关性,水深和土壤速效钾是影响湿地植物群落分布格局的主要因子。群落物种多样性指数与环境因子之间存在显著相关性,群落物种丰富度指数随水深增加呈先增后降的趋势,群落物种均匀度指数与水质氨氮浓度之间呈负相关关系。本研究旨在揭示浪畔湖国家重要湿地植物多样性与环境因子之间的生态关系,为保护湿地珍稀物种及维持湿地生态系统稳定提供理论依据。

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.

Analysis on Chemical Compositions in Chinese Wolfberry from Different Producing Areas

[Objective] This study aimed to conduct correspondence cluster analysis of the trace elements in Chinese wolfberry from Qinghai and Ningxia regions, and to investigate the relationship among the quality of the wolfberry samples, the composition of trace elements and the sample sources. [Method] The determined contents of trace elements and ratios of zinc to copper （Zn/Cu） of wolfberry from 11 different producing areas of Qinghai and Ningxia regions were adopted to construct the raw measurement data matrix, to analyze the distribution characteristics of the trace ele- ments in wolfberry from Qinghai and Ningxia by using the corresponding cluster analysis method. [Result] The quality of wolfberry samples in 7hongning County, Zhongwei City, Pingluo County, Shizuishan City, Heicheng Town of Ningxia Hui Au-tonomous Region and Hehuang Valley, Golmud City of Qinghai Province is mainly related to the contents of Zn and Mn; Zn/Cu greatly affects the quality of Chinese wolfberry in Dulan County of Qinghai Province; Fe has great effect on the quality of Chinese wolfberry in Yinchuan City of Ningxia Hui Autonomous Region; Cu greatly affects the quality of Chinese wolfberry in Nuomuhong Village of Qinghai Province and a wolfberry research institute in Ningxia. [Conclusion] The relationship between the quality of wolfberry from different producing areas and the trace elements was investigated, which provides theoretical and practical basis for the cultivation, har- vesting, processing, and further development and utilization of Chinese wolfberry resources from different producing areas.