Integrated statistical and hydro-geochemical approach to identify the origin and process of saline contamination of Remila plain groundwater (Khenchela, Algeria)

2021 ◽  
pp. 1-11
Author(s):  
Laiche AOUIDANE ◽  
Mohamed BELHAMRA ◽  
Asma KHEDDOUMA
Keyword(s):  
Clustering Analysis ◽  
Statistical Approach ◽  
Saline Water ◽  
Principal Component ◽  
Saline Intrusion ◽  
Ionic Exchange ◽  
Southeastern Part ◽  
Northeastern Part ◽  
Semi Arid Region ◽  
Geochemical Approach

ABSTRACT Groundwater is widely used in the semi-arid region of Remila plain (Khenchela, Algeria) for urban and agricultural supplies. An integrated statistical and hydro-geochemical approach was performed with 70 water samples in order to identify the main processes and the origin of water salinisation. The results have suggested the dominance of three chemical facies: Sulphato cloruro calcic (SO4–Cl–Ca) in the northeastern part, Sulphato cloruro calci magnisian (SO–4Cl–Ca–Mg) in most of the waters andalkali-earth bicarbonate (HCO3–Ca–Mg) in the southeastern part. Although based on principal component analysis and hierarchical clustering analysis, the statistical approach identified three water groups: (1) saline water (17 %; total dissolved solids >1000 mg l−1 with the dominance of Sulphate (SO42−)); (2) moderately saline water (17 %) with a dominance of bicarbonate (HCO3−); and (3) moderately saline water (66 %) with mixed facies. The binary diagrams confirmed the predominance of three processes: evaporite dissolution and/or precipitation, combined by ionic exchange. In the northeastern part of the area, however, another process was detected – the saline intrusion of Sabkha water, favoured by extensive groundwater use.

Spatial clustering analysis of green economy based on knowledge graph

2021 ◽  
pp. 1-10
Author(s):  
Shiyuan Zhou ◽  
Xiaoqin Yang ◽  
Qianli Chang
Keyword(s):  
Spatial Data ◽  
Clustering Analysis ◽  
Spatial Clustering ◽  
Evaluation Model ◽  
Principal Component ◽  
Green Economy ◽  
Membership Degree ◽  
Two Dimensional ◽  
Dimensional Graph ◽  
Improved Algorithm

By organically combining principal component analysis, spatial autocorrelation algorithm and two-dimensional graph theory clustering algorithm, the comprehensive evaluation model of regional green economy is explored and established. Based on the evaluation index system of regional green economy, this paper evaluates the development of regional green economy comprehensively by using principal component analysis, and evaluates the competitive advantage of green economy and analyzes the spatial autocorrelation based on the evaluation results. Finally, the green economy and local index score as observed values, by using the method of two-dimensional graph clustering analysis of spatial clustering. In view of the fuzzy k –modes cluster membership degree measure method without considering the defects of the spatial distribution of object, double the distance and density measurement of measure method is introduced into the fuzzy algorithm of k –modes, thus in a more reasonable way to update the membership degree of the object. Vote, MUSH-ROOM and ZOO data sets in UCI machine learning library were used for testing, and the F value of the improved algorithm was better than that of the previous one, indicating that the improved algorithm had good clustering effect. Finally, the improved algorithm is applied to the spatial data collected from Baidu Map to cluster, and a good clustering result is obtained, which shows the feasibility and effectiveness of the algorithm applied to spatial data. Results show that the development of green economy using the analysis method of combining quantitative analysis and qualitative analysis, explores the connotation of green economy with space evaluation model is feasible, small make up for the qualitative analysis of the green economy in the past, can objective system to reflect the regional green economic development level, will help policy makers scientific formulating regional economic development strategy, green integrated development of regional green economy from the macroscopic Angle, the development of network system.

scholarly journals Clustering analysis of the distribution characteristics of unobserved economic regions in China——Based on multi-indicator panel data

2021 ◽  
Vol 275 ◽  
pp. 01072
Author(s):  
Yang Fan
Keyword(s):  
Panel Data ◽  
Clustering Analysis ◽  
Principal Component ◽  
Entropy Method ◽  
Practical Significance ◽  
Economic Governance ◽  
Factors Affecting ◽  
Use Of Resources ◽  
Economic Regions ◽  
Component Feature

The existence of unobserved economy is one of the important factors affecting GDP calculation. This paper uses the provincial panel data from 2010 to 2019 in China, and adopts the method of principal component feature extraction to carry out cluster analysis on the multi-indicator panel data. This method preserves the dynamic characteristics of the panel data, calculates the comprehensive score of each eigenvalue, and gives weight to the eigenvalue by using the entropy method, so as to optimize the clustering results representing the eight indicators of the unobserved economy. Through the analysis, it is found that the regional development of China’s unobserved economy is obviously different, and each type has different influencing factors. This result has important practical significance for different regions in China to formulate differentiated unobserved economic governance policies. This also helps to make better use of resources and develop an energy-saving economy.

scholarly journals Gas exchanges and growth of cotton cultivars under water salinity

2019 ◽  
Vol 23 (6) ◽  
pp. 393-399
Author(s):  
André A. R. da Silva ◽  
Luana L. de S. A. Veloso ◽  
Ronaldo do Nascimento ◽  
Elka C. S. Nascimento ◽  
Carlos V. de C. Bezerra ◽  
...  
Keyword(s):  
Irrigation Water ◽  
Sandy Loam ◽  
Saline Water ◽  
Water Salinity ◽  
Gas Exchanges ◽  
Randomized Design ◽  
Irrigation Water Salinity ◽  
Completely Randomized Design ◽  
Semi Arid Region ◽  
Response To Water

ABSTRACT Indication of salt-tolerant cotton cultivars can make the agricultural exploitation with saline water irrigation feasible in the Brazilian semi-arid region. Thus, this study aimed to evaluate the gas exchanges and growth of cotton cultivars irrigated with saline water. The study was conducted in pots adapted as drainage lysimeters under greenhouse conditions, using a sandy loam Entisols as substrate. Treatments were distributed in completely randomized design, in 5 x 2 factorial arrangement, relative to five levels of irrigation water electrical conductivity - ECw (1.5, 3.0, 4.5, 6.0 and 7.5 dS m-1) and two cotton cultivars (BRS 368 RF and BRS Safira). Increase in irrigation water salinity inhibits the vegetative growth and gas exchanges of the cotton cultivars BRS Safira and BRS 368 RF. Leaf area and instantaneous carboxylation efficiency are the most affected variables. Physiological and growth performance of the cultivar BRS Safira in response to water salinity was higher than that of BRS 368 RF.

A COMBINED PCA–ICA STATISTICAL APPROACH AND QUADRATIC SPLINE WAVELETS FOR DETECTION OF R-PEAKS AND HEART RATE ESTIMATIONS IN ELECTROCARDIOGRAMS

2011 ◽  
Vol 11 (03) ◽  
pp. 625-642 ◽  
Author(s):  
MANENDRAPAL SINGH CHAWLA
Keyword(s):  
Heart Rate ◽  
Statistical Approach ◽  
Principal Component ◽  
Quadratic Spline ◽  
Redundant Data ◽  
Correlated Channels ◽  
Spline Wavelets ◽  
Threshold Adjustment ◽  
Ica Algorithm ◽  
Ecg Data

The need for the possible improvements in the proposed algorithm is felt toward more effective filtering in the principal component analysis (PCA) preprocessing stage itself, as well for better variance threshold adjustment. Using composite wavelet transform (WT)-based PCA–ICA methods helps for redundant data reduction as well for better feature extraction. This article discusses some of the conditions of ICA that could affect the reliability of the separation and evaluation of issues related to the properties of the signals and number of sources. In this analysis, a new statistical algorithm is proposed, based on the use of combined PCA–ICA for the three correlated channels of 12-channel electrocardiographic (ECG) data. This study also deals with the detection of QRS complexes in electrocardiograms using combined PCA–ICA algorithm. The efficacy of the combined PCA–ICA algorithm lies in the fact that the location of the R-peaks is accurately determined, and none of the peaks are ignored or missed, as quadratic spline wavelet is also used. With (WT)-based methods, PCA and ICA are used not only for preprocessing, but may also be used for postprocessing based on the requirements, whether ICA is used first then PCA or vice versa.

scholarly journals Seasonal dynamics of cyanobacteria in a eutrophic reservoir (Arcoverde) in a semi-arid region of Brazil

2012 ◽  
Vol 72 (3) ◽  
pp. 533-544 ◽  
Author(s):  
MC. Bittencourt-Oliveira ◽  
SN. Dias ◽  
AN. Moura ◽  
MK. Cordeiro-Araújo ◽  
EW. Dantas
Keyword(s):  
Phytoplankton Community ◽  
Climatic Factors ◽  
Principal Component ◽  
Taxonomic Composition ◽  
Dry Season ◽  
Northeastern Brazil ◽  
Cylindrospermopsis Raciborskii ◽  
Physical And Chemical Variables ◽  
Semi Arid Region ◽  
Cyanobacterial Biomass

Environmental conditions favor the predominance of dense populations of cyanobacteria in reservoirs in northeastern Brazil. The aim of this study was to understand cyanobacterial population dynamics in the rainy and dry seasons at two depths in the Arcoverde reservoir. Microalgae and cyanobacteria samples were collected during 24 hours with intervals of 4 hours (nycthemeral) at sub-surface and 10 m using a van Dorn bottle and a determined biomass. Physical and chemical variables were obtained and the data were analyzed using the principal component analysis (PCA). No nycthemeral variations in the taxonomic composition or distribution of the populations of cyanobacteria were found between the different times of day in either the rainy or dry season. In both seasons, the greatest biomass of the phytoplankton community was made up of cyanobacteria at two depths and all times of the day. Cylindrospermopsis raciborskii (Woloszynska) Seenayya et Subba Raju was dominant at all times of the day on both the surface and at the bottom. In the rainy season, the differences in cyanobacterial biomass between the surface and bottom were less significant than in the dry season. The differences in cyanobacterial biomass between surface and bottom were less pronounced than those found in the dry season. We concluded that a) physical variables better explain the alterations of species in the phytoplankton community in an environment dominated by cyanobacteria throughout the year; b) seasonal climatic factors associated to periods of stratification and de-stratification are important for alterations in the community and variations in biomass and, c) the turbidity caused by rainfall favored the emergence and establishment of other cyanobacteria, especially Planktothrix agardhii (Gomont) Anagnostidis & Komárek.

scholarly journals Metabolomics Approach for Discrimination and Quality Control of Natural and Commercial Fallopia multiflora Products in Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Nguyen Thi Thoa ◽  
Nguyen Hai Dang ◽  
Do Hoang Giang ◽  
Nguyen Thi Thu Minh ◽  
Nguyen Tien Dat
Keyword(s):  
Clustering Analysis ◽  
Principal Component ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Fda Guidance ◽  
Promising Tool ◽  
Precision And Accuracy ◽  
And Control

A precise HPLC-DAD-based quantification together with the metabolomics statistical method was developed to distinguish and control the quality of Fallopia multiflora, a popular medicinal material in Vietnam. Multivariate statistical methods such as hierarchical clustering analysis and principal component analysis were utilized to compare and discriminate six natural and twelve commercial samples. 2,3,4′,5-Tetrahydroxystilbene 2-O-β-D-glucopyranoside (THSG) (1), emodin (4), and the new compound 6-hydroxymusizin 8-O-α-D-apiofuranosyl-(1⟶6)-β-D-glucopyranoside (5) could be considered as important markers for classification of F. multiflora. Furthermore, seven phenolics were quantified that the variation in the contents of selected metabolites revealed the differences in the quality of natural and commercial samples. Recovery of the compounds from the analytes was more than 98%, while the limits of detection (LOD) and the limits of quantitation (LOQ) ranged from 0.5 to 6.6 μg/ml and 1.5 to 19.8 μg/ml, respectively. The linearity, LOD, LOQ, precision, and accuracy satisfied the criteria FDA guidance on bioanalytical methods. Overall, this method is a promising tool for discrimination and quality assurance of F. multiflora products.

Cluster Analysis of the Chrysalis Silk Production Provinces in China

2013 ◽  
Vol 796 ◽  
pp. 323-326
Author(s):  
Huan Wang ◽  
Ting Ting Tao ◽  
Wan Chun Fei
Keyword(s):  
Principal Components ◽  
Clustering Analysis ◽  
Structural Adjustment ◽  
Price Index ◽  
Consumer Price Index ◽  
Principal Component ◽  
Original Data ◽  
Basic Reference ◽  
Per Capita ◽  
Production Areas

In this article, the yield of mulberry cocoon, the output of raw silk, the output of silk fabric, the consumer price index, the GDP per capita and the per capita income from 1999 to 2011 were analyzed for their principal components on the major production areas of cocoon and silk in China. The principal component analysis can ensure the smallest loss of the original data, to replace the multi-variables with a few synthetic variables, to simplify the data structure, and objectively determine the weights. The distances and similarities between provincial principal components, which were regarded as multivariable time series, were analyzed and computed, and clustering analysis were carried out. The result can be used as a basic reference for the industrial configuration and structural adjustment of silk in China.

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