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 address...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.展开更多
In recent years,when planning and determining a travel destination,residents often make the best of Internet techniques to access extensive travel information.Search engines undeniably reveal visitors'real-time pr...In recent years,when planning and determining a travel destination,residents often make the best of Internet techniques to access extensive travel information.Search engines undeniably reveal visitors'real-time preferences when planning to visit a destination.More and more researchers have adopted tourism-related search engine data in the field of tourism prediction.However,few studies use search engine data to conduct cluster analysis to identify residents'choice toward a tourism destination.In the present study,146 keywords related to“Beijing tourism”are obtained from Baidu index and principal component analysis(PCA)is applied to reduce the dimensionality of keywords obtained by Baidu index.Modified affinity propagation(MAP)clustering algorithm is used to classify provinces into several groups to identify the choice of residents to travel to Beijing.The result shows that residents in Hebei province are most likely to travel to Beijing.The cluster result also shows that PCA–MAP performs better than other clustering methods such as K-means,linkage,and Affinity Propogation(AP)in terms of silhouette coefficient and Calinski–Harabaz index.We also distinguish the difference of residents’choice to travel to Beijing during the peak tourist season and off-season.The residents of Tianjing are inclined to travel to Beijing during the peak tourist season.The residents of Guangdong,Hebei,Henan,Jiangsu,Liaoning,Shanghai,Shandong,and Zhejiang have high attention to travel to Beijing during both seasons.展开更多
The exploitation of systems using solar energy as a source of energy is not fluctuations free because of short passage of clouds on solar radiation. The amplitude, the persistence and the frequency of these fluctuatio...The exploitation of systems using solar energy as a source of energy is not fluctuations free because of short passage of clouds on solar radiation. The amplitude, the persistence and the frequency of these fluctuations should be analyzed with appropriate tools, instead of focusing on their location over time. The analysis of these fluctuations should use the instantaneous clearness index whose distribution is given as a first approximation which is independent not only of the season but also of the site. It is important to evaluate the potential solar energy in a region. Indeed such evaluation helps the decision-makers in their reflections on agricultural or photovoltaic solar projects. Then this study was conducted for a predictive purpose. The method used in our work combines the classification method which is the hierarchical ascending classification and two partitioning methods, the principal component?analysis and the K-means method. The partitioning method enabled to?achieve a number of well-known situations (in advance) that are representative of the day. The study was based on the data of a climatic weather station in the district of Yamoussoukro located in the center region of Côte d’Ivoire during the 2017 year. Using the clearness index, the study allowed the classification of the solar radiation in the region. Thus, it showed that only 346 days of the 365 days in 2017 were classified (95%). We identified three clusters of days, the cloudy sky (29%), the partly cloudy sky?(32%) and the clear sky (39%). The statistical tests used for the characterization?of these clusters will be detailed in a future study.展开更多
Nowadays the human activity has increased the pressure on surface water quality. The purpose of this study is to assess the environmental quality of the Seman River water (in Southern part of Albania) through a 5-year...Nowadays the human activity has increased the pressure on surface water quality. The purpose of this study is to assess the environmental quality of the Seman River water (in Southern part of Albania) through a 5-year monitoring program of 14 parameters (pH, DO, EC, TSS, Cl<sup>-</sup>, <span style="white-space:nowrap;">NO<sup>-</sup><sub style="margin-left:-7px;">3</sub></span>, Total-N, Total-P, BOD<sub>5</sub>, Cu<sup>2+</sup>, Ni<sup>2+</sup>, Pb<sup>2+</sup>, Cd<sup>2+</sup> and Temp. <span style="white-space:nowrap;">°</span>C), that determine the environmental status of this waterbody, as well as the application of WQI (CCME) through a multivariable approach. Based on the cluster dendogram results, it can be concluded that during wet seasons such as winter-spring, there are more sediments which influence other physic-chemical parameters, while during dry seasons (summer-autumn) there are more decomposition reactions of elements released by sediments and influenced by temperature. PCA analysis determines whether the groups of factors correlate strongly or not, depending on the internal structures of the groups and variables “heavy” or latent and vary from season to season with differentiated contributions to the water quality. All three factors influence WQI to the extent of 56% in the summer and spring season and 64% and 40% in the autumn and winter season, respectively.展开更多
To categorize the nations to reflect the development status, to date, there are many conceptual frameworks. The Human Development index (HDI) that is published by the United Nations Development Programme is widely acc...To categorize the nations to reflect the development status, to date, there are many conceptual frameworks. The Human Development index (HDI) that is published by the United Nations Development Programme is widely accepted and practiced by many people such as academicians, politicians, and donor organizations. However, though the development of HDI has gone through many revisions since its formulation in 1990, even the current version of the index formulation published in 2016 needs research to better understand and to gap-fill the knowledge base that can enhance the index formulation to facilitate the direction of attention such as release of funds. Therefore, in this paper, based on principal component analysis and K-means clustering algorithm, the data that reflect the measures of life expectancy index (LEI), education index (EI), and income index (II) are analyzed to categorize and to rank the member states of the UN using R statistical software package, an open source extensible programming language for statistical computing and graphics. The outcome of the study shows that the proportion of total eigen value (i.e., proportion of total variance) explained by PCA-1 (i.e., first principal component) accounts for more than 85% of the total variation. Moreover, the proportion of total eigen value explained by PCA-1 increases with time (i.e., yearly) though the amount of increase with time is not significant. However, the proportions of total eigen value explained by PCA-2 and PCA-3 decrease with time. Therefore, the loss of information in choosing PCA-1 to represent the chosen explanatory variables (i.e., LEI, EI, and II) may diminish with time if the trend of increasing pattern of proportion of total eigen value explained by PCA-1 with time continues in the future as well. On the other hand, the correlation between EI and PCA-1 increases with time although the magnitude of increase is not that significant. This same trend is observed in II as well. However, in contrast to these observations, the correlation between PCA-1 and LEI decreases with time. These findings imply that the contributions of EI and II to PCA-1 increase with time, but the contribution of LEI to PCA-1 decreases with time. On top of these, as per Hopkins statistic, the clusterability of the information conveyed by PCA-1 alone is far better than the clusterability of the information conveyed by PCA scores (i.e., PCA-1, PCA-2, and PCA-3) and the explanatory variables. Therefore, choosing PCA-1 to represent the chosen explanatory variables is becoming more concrete.展开更多
目的建立高效液相一测多评法(high performance liquid chromatography-quantitative analysis of multi-components by single marker,HPLC-QAMS)定量测定甘露消渴胶囊中毛蕊花糖苷、焦地黄苯乙醇苷B_(1)、东莨菪素、东莨菪苷、莫诺苷...目的建立高效液相一测多评法(high performance liquid chromatography-quantitative analysis of multi-components by single marker,HPLC-QAMS)定量测定甘露消渴胶囊中毛蕊花糖苷、焦地黄苯乙醇苷B_(1)、东莨菪素、东莨菪苷、莫诺苷、马钱苷、山茱萸新苷、泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B的含量,并结合化学计量学对3个厂家的产品质量进行评价。方法采用高效液相色谱仪,流动相选择乙腈-0.2%磷酸,色谱柱为Ultimate XB-C_(18)(250 mm×4.6 mm,5μm)进行梯度洗脱。检测波长分别为330 nm(检测毛蕊花糖苷、焦地黄苯乙醇苷B_(1)、东莨菪素、东莨菪苷)、240 nm(检测莫诺苷、马钱苷、山茱萸新苷)和208 nm(检测泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B)。以马钱苷为内参物,建立其他10种成分的相对校正因子,并计算各成分含量。采用SPSS 26.0统计软件对甘露消渴胶囊中11种成分含量测定结果进行聚类分析和主成分分析。结果毛蕊花糖苷、焦地黄苯乙醇苷B_(1)、东莨菪素、东莨菪苷、莫诺苷、马钱苷、山茱萸新苷、泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B分别在7.51~375.50、3.37~168.50、0.46~23.00、1.08~54.00、9.60~480.00、4.77~238.50、1.64~82.00、0.76~38.00、1.95~97.50、0.93~46.50和6.69~334.50μg·mL^(-1)范围内线性关系良好,相关系数(r=0.9991~0.9995),加样回收率及相应的RSD依次为99.11%(1.24%)、98.66%(1.45%)、97.71%(1.60%)、96.98%(0.93%)、100.20%(0.65%)、98.54%(1.18%)、97.90%(1.34%)、96.95%(1.07%)、98.47%(0.94%)、99.10%(0.89%)和100.08%(0.59%)。15批甘露消渴胶囊聚为3类,经主成分分析得4个主成分的累积贡献率达到85.338%。结论所建立的HPLC-QAMS法结合化学计量学可综合评价甘露消渴胶囊的质量。展开更多
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 coverag...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.展开更多
文摘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.
基金Humanities and Social Sciences Foundation of Chinese Ministry of Education,China(No.18YJA630005)National Natural Science Foundation of China(No.71810107003).
文摘In recent years,when planning and determining a travel destination,residents often make the best of Internet techniques to access extensive travel information.Search engines undeniably reveal visitors'real-time preferences when planning to visit a destination.More and more researchers have adopted tourism-related search engine data in the field of tourism prediction.However,few studies use search engine data to conduct cluster analysis to identify residents'choice toward a tourism destination.In the present study,146 keywords related to“Beijing tourism”are obtained from Baidu index and principal component analysis(PCA)is applied to reduce the dimensionality of keywords obtained by Baidu index.Modified affinity propagation(MAP)clustering algorithm is used to classify provinces into several groups to identify the choice of residents to travel to Beijing.The result shows that residents in Hebei province are most likely to travel to Beijing.The cluster result also shows that PCA–MAP performs better than other clustering methods such as K-means,linkage,and Affinity Propogation(AP)in terms of silhouette coefficient and Calinski–Harabaz index.We also distinguish the difference of residents’choice to travel to Beijing during the peak tourist season and off-season.The residents of Tianjing are inclined to travel to Beijing during the peak tourist season.The residents of Guangdong,Hebei,Henan,Jiangsu,Liaoning,Shanghai,Shandong,and Zhejiang have high attention to travel to Beijing during both seasons.
文摘The exploitation of systems using solar energy as a source of energy is not fluctuations free because of short passage of clouds on solar radiation. The amplitude, the persistence and the frequency of these fluctuations should be analyzed with appropriate tools, instead of focusing on their location over time. The analysis of these fluctuations should use the instantaneous clearness index whose distribution is given as a first approximation which is independent not only of the season but also of the site. It is important to evaluate the potential solar energy in a region. Indeed such evaluation helps the decision-makers in their reflections on agricultural or photovoltaic solar projects. Then this study was conducted for a predictive purpose. The method used in our work combines the classification method which is the hierarchical ascending classification and two partitioning methods, the principal component?analysis and the K-means method. The partitioning method enabled to?achieve a number of well-known situations (in advance) that are representative of the day. The study was based on the data of a climatic weather station in the district of Yamoussoukro located in the center region of Côte d’Ivoire during the 2017 year. Using the clearness index, the study allowed the classification of the solar radiation in the region. Thus, it showed that only 346 days of the 365 days in 2017 were classified (95%). We identified three clusters of days, the cloudy sky (29%), the partly cloudy sky?(32%) and the clear sky (39%). The statistical tests used for the characterization?of these clusters will be detailed in a future study.
文摘Nowadays the human activity has increased the pressure on surface water quality. The purpose of this study is to assess the environmental quality of the Seman River water (in Southern part of Albania) through a 5-year monitoring program of 14 parameters (pH, DO, EC, TSS, Cl<sup>-</sup>, <span style="white-space:nowrap;">NO<sup>-</sup><sub style="margin-left:-7px;">3</sub></span>, Total-N, Total-P, BOD<sub>5</sub>, Cu<sup>2+</sup>, Ni<sup>2+</sup>, Pb<sup>2+</sup>, Cd<sup>2+</sup> and Temp. <span style="white-space:nowrap;">°</span>C), that determine the environmental status of this waterbody, as well as the application of WQI (CCME) through a multivariable approach. Based on the cluster dendogram results, it can be concluded that during wet seasons such as winter-spring, there are more sediments which influence other physic-chemical parameters, while during dry seasons (summer-autumn) there are more decomposition reactions of elements released by sediments and influenced by temperature. PCA analysis determines whether the groups of factors correlate strongly or not, depending on the internal structures of the groups and variables “heavy” or latent and vary from season to season with differentiated contributions to the water quality. All three factors influence WQI to the extent of 56% in the summer and spring season and 64% and 40% in the autumn and winter season, respectively.
文摘To categorize the nations to reflect the development status, to date, there are many conceptual frameworks. The Human Development index (HDI) that is published by the United Nations Development Programme is widely accepted and practiced by many people such as academicians, politicians, and donor organizations. However, though the development of HDI has gone through many revisions since its formulation in 1990, even the current version of the index formulation published in 2016 needs research to better understand and to gap-fill the knowledge base that can enhance the index formulation to facilitate the direction of attention such as release of funds. Therefore, in this paper, based on principal component analysis and K-means clustering algorithm, the data that reflect the measures of life expectancy index (LEI), education index (EI), and income index (II) are analyzed to categorize and to rank the member states of the UN using R statistical software package, an open source extensible programming language for statistical computing and graphics. The outcome of the study shows that the proportion of total eigen value (i.e., proportion of total variance) explained by PCA-1 (i.e., first principal component) accounts for more than 85% of the total variation. Moreover, the proportion of total eigen value explained by PCA-1 increases with time (i.e., yearly) though the amount of increase with time is not significant. However, the proportions of total eigen value explained by PCA-2 and PCA-3 decrease with time. Therefore, the loss of information in choosing PCA-1 to represent the chosen explanatory variables (i.e., LEI, EI, and II) may diminish with time if the trend of increasing pattern of proportion of total eigen value explained by PCA-1 with time continues in the future as well. On the other hand, the correlation between EI and PCA-1 increases with time although the magnitude of increase is not that significant. This same trend is observed in II as well. However, in contrast to these observations, the correlation between PCA-1 and LEI decreases with time. These findings imply that the contributions of EI and II to PCA-1 increase with time, but the contribution of LEI to PCA-1 decreases with time. On top of these, as per Hopkins statistic, the clusterability of the information conveyed by PCA-1 alone is far better than the clusterability of the information conveyed by PCA scores (i.e., PCA-1, PCA-2, and PCA-3) and the explanatory variables. Therefore, choosing PCA-1 to represent the chosen explanatory variables is becoming more concrete.
基金Funded by 973 Program of Ministry of National Defense of China(Grant No.613237)
文摘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.
