scholarly journals Evaluation of the quality of superficial sediment using principal components analysis of the physical–chemical attributes of the Mearim River – Brazil

2020 ◽  
Vol 42 ◽  
pp. e5
Author(s):  
Neuriane Silva Lima ◽  
Darlan Ferreira da Silva ◽  
Wallace Ribeiro Nunes Neto ◽  
Delmo Matos da Silva ◽  
Leila Cristina Almeida de Sousa ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Rainy Season ◽  
Principal Component ◽  
Component Analysis ◽  
Dry Period ◽  
Data Set ◽  
Inorganic Matter ◽  
Sampling Points

The Mearim River is one of the main rivers of Maranhão, which, over the years, has been affected from pollution caused by human activities such as deforestation, disposal of domestic effluents, and agricultural activities, among others. The objective of this research was to evaluate the environmental quality of the Mearim River through the study of the sediment in different periods. In order to investigate this question, four sampling points (P1- Balneário, P2-Cais, P3-Trizidela, and P4-Matadouro) were submitted to particle size analysis (clay, silt, and fine sand) and physico-chemical analyses (pH, organic matter, and inorganic and organic carbon). Two principal components were generated in principal component analysis, explaining 73% of the total variance among the parameters within the studied periods. The overall analysis of the data set by principal component analysis highlighted two clusters: one relating the attributes to three sampling points analyzed in the rainy season and another relating the attributes to four sampling points analyzed in the dry period. Multivariate analysis of the data showed that the orga­­­­­­­nic matter, clay, and pH parameters were directly correlated with the dry period (correlation coefficients 0.41), and inorganic matter (correlation coefficient = | 0,414) was more sensitive in the rainy season.

scholarly journals Assessment of subjective aspects of the quality of life in the various regions of the Czech Republic

2011 ◽  
Vol 59 (7) ◽  
pp. 267-274 ◽  
Author(s):  
Julie Poláčková ◽  
Andrea Jindrová
Keyword(s):  
Quality Of Life ◽  
Principal Component Analysis ◽  
Czech Republic ◽  
Social Services ◽  
Principal Component ◽  
Well Being ◽  
Component Analysis ◽  
Data Set ◽  
The Czech Republic

The paper is focused on the methodological approaches to assess subjective aspects of the quality of life in the various regions. Besides, directly measurable indicators, which may not always correspond with the quality of life of the individuals in the regions, the subjective aspects of well-being are also in the spotlight. The pilot analysis examined the answers to questions such as: Are you satisfied with the health and social services, the cost of living, safety of public spaces, affordability of housing, or your personal job situation? These answers were used for an assessment of the quality of life in the different regions of the Czech Republic. We used multivariate modeling to explicitly account for the hierarchical structure of respondents within the Czech Republic, and for understanding patterns of variation between regions. The principal component analysis (PCA) was used for the general analysis of regional differences. The overall goal of principal component analysis is to reduce the dimensionality of a data set, while simultaneously retaining the information present in the data. The differences were illustrated by cartographic visualization and by scatter plots of the first three principal components. The cluster analysis was used to discover similarities and differences of the quality of life within various regions of the Czech Republic.

Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration

2017 ◽  
Vol 16 (3) ◽  
Author(s):  
Shofiqul Islam ◽  
Sonia Anand ◽  
Jemila Hamid ◽  
Lehana Thabane ◽  
Joseph Beyene
Keyword(s):  
Principal Component Analysis ◽  
Data Integration ◽  
Principal Components ◽  
Mirna Expression ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Data Sets ◽  
Data Set ◽  
Multiple Data Sets

AbstractLinear principal component analysis (PCA) is a widely used approach to reduce the dimension of gene or miRNA expression data sets. This method relies on the linearity assumption, which often fails to capture the patterns and relationships inherent in the data. Thus, a nonlinear approach such as kernel PCA might be optimal. We develop a copula-based simulation algorithm that takes into account the degree of dependence and nonlinearity observed in these data sets. Using this algorithm, we conduct an extensive simulation to compare the performance of linear and kernel principal component analysis methods towards data integration and death classification. We also compare these methods using a real data set with gene and miRNA expression of lung cancer patients. First few kernel principal components show poor performance compared to the linear principal components in this occasion. Reducing dimensions using linear PCA and a logistic regression model for classification seems to be adequate for this purpose. Integrating information from multiple data sets using either of these two approaches leads to an improved classification accuracy for the outcome.

scholarly journals Key Determinants of Indonesia’s Banks Financial Performance

2019 ◽  
Vol 21 (2) ◽  
pp. 58-67
Author(s):  
Martin Panggabean ◽  
Stefan Batara Panggabean
Keyword(s):  
Principal Component Analysis ◽  
Financial Performance ◽  
Total Variation ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Financial Indicators ◽  
Balance Sheets ◽  
Conventional Banks

Depositors, investors, as well as public in general need easily accessible indicators that are important to differentiate various banks. This research addresses simultaneously two important issues: analyzing and identifying which key publicly available financial indicators of banks are important, as well as approximating the weight of the aforementioned indicators when banks’ comparisons are to be made. Utilizing the recent 2017 database from 90 conventional banks, this study analyzes 17 banking ratios using the method of principal component analysis. The calculations show that five components explain around 75 percent of total variation in the data. Those five components represent indicators on profitability, quality of capital, quality of loans, fee-based activities, and liquid assets in the balance sheets. Further, by combining five principal components, the result shows that even small banks can achieve good financial performances.

scholarly journals Water quality assessment of an urban river receiving tail water using the single-factor index and principal component analysis

2018 ◽  
Vol 19 (2) ◽  
pp. 603-609 ◽  
Author(s):  
Weiwei Lu ◽  
Juan Wu ◽  
Zhu Li ◽  
Naxin Cui ◽  
Shuiping Cheng
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Urban River ◽  
Single Factor ◽  
Sampling Points ◽  
Factor Index ◽  
Tail Water

Abstract Tail water from wastewater treatment plants (WWTP) serves as a major supplementary water source for scenic water bodies, whose water quality is one of the major focuses of public and scientific inquiries. This study investigated the temporal and spatial variations in water quality of Tangxihe River, a eutrophic urban river receiving tail water from a nearby WWTP in Hefei City, using the single-factor index (SFI) and principal component analysis (PCA). The results of SFI indicated that the most important parameters responsible for low water quality were total nitrogen (TN) and ammonia (NH4+-N). PCA showed that tail water from the WWTP greatly reduced water quality, as demonstrated by the significantly increased SFIs and integrated principal component values (F values) of the sampling points around the drain outlet of the WWTP (T3, T4 and T5). The sampling points located at the upstream of the river (T1) and up the water-gate of Chaohu Lake (T6) had negative F values, indicating relatively higher water quality. In addition, the season had a significant effect on the water quality of the river. Moreover, we discuss measures to improve the water quality of urban rivers in order to maintain their ecological functions.

scholarly journals Evaluation of Edible Quality of Sorghum Based on Principal Component Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Chunlei Ni ◽  
Shan Zhang ◽  
Gaopeng Zhang ◽  
Jianjun Cheng ◽  
Huanyu Zheng
Keyword(s):  
Principal Component Analysis ◽  
Sorghum Bicolor ◽  
Sensory Evaluation ◽  
Principal Components ◽  
Northeast China ◽  
Principal Component ◽  
Component Analysis ◽  
Contribution Rate ◽  
Cooking Properties

Sorghum (Sorghum bicolor (L.) Moench) is one of the most important cereals in the Northeast China. The physicochemical, pasting, texture, and cooking properties of 21 sorghum varieties were determined, which were mainly cultivated in Northeast China. Then, the evaluation of edible quality of sorghum was based on principal component analysis and fitted with the score of sensory evaluation. Five principal components (PCs) with a cumulative contribution rate of 86.19% could be picked out to describe the taste, pasting, flavor, cooking, and variety of sorghum, respectively. And a comprehensive equation of sorghum edible quality in Northeast China was constructed which was Z = 0.45F1 + 0.25F2 + 0.12F3 + 0.10F4 + 0.08F5. The edible quality of No. 14 and No. 15 was the best. The sensory evaluation was used to verify the above equation with the fitting coefficient of 0.81, which indicated that the equation could be more accurate to evaluate the edible quality of sorghum in Northeast China.

scholarly journals Kernel principal component analysis for multimedia retrieval

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Guang-Ho Cha
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Principal Components ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Multimedia Retrieval ◽  
Kernel Principal Component Analysis ◽  
Kernel Pca ◽  
Data Set

Principal component analysis (PCA) is an important tool in many areas including data reduction and interpretation, information retrieval, image processing, and so on. Kernel PCA has recently been proposed as a nonlinear extension of the popular PCA. The basic idea is to first map the input space into a feature space via a nonlinear map and then compute the principal components in that feature space. This paper illustrates the potential of kernel PCA for dimensionality reduction and feature extraction in multimedia retrieval. By the use of Gaussian kernels, the principal components were computed in the feature space of an image data set and they are used as new dimensions to approximate image features. Extensive experimental results show that kernel PCA performs better than linear PCA with respect to the retrieval quality as well as the retrieval precision in content-based image retrievals.Keywords: Principal component analysis, kernel principal component analysis, multimedia retrieval, dimensionality reduction, image retrieval

scholarly journals Rime samples characterization and comparison using classical and fuzzy principal components analysis

2008 ◽  
Vol 6 (2) ◽  
pp. 208-215 ◽  
Author(s):  
Kamila Klimaszewska ◽  
Costel Sârbu ◽  
Żaneta Polkowska ◽  
Marek Błaś ◽  
Mieczysław Sobik ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Direct Consequence ◽  
Component Analysis ◽  
Component Model ◽  
Total Variance ◽  
Data Set ◽  
Components Analysis ◽  
Fuzzy Principal Component Analysis

AbstractThe main objective of this paper is to introduce principal component analysis and two robust fuzzy principal component algorithms as useful tools in characterizing and comparing rime samples collected in different locations in Poland (2004–2007). The efficiency of the applied procedures was illustrated on a data set containing 108 rime samples and concentration of anions, cations, HCHO, as well as pH and conductivity. The fuzzy principal component algorithms achieved better results mainly because they are more compressible than classical PCA and very robust to outliers. For example, a three component model, fuzzy principal component analysis-first component (FPCA-1) accounts for 62.37% of the total variance and fuzzy principal component analysis-orthogonal (FPCA-o) 90.11%; PCA accounts only for 58.30%. The first two principal components explain 51.41% of the total variance in the case of FPCA-1 and 79.59% in the case of FPCA-o as compared to only 47.55% for PCA. As a direct consequence, PCA showed only a partial differentiation of rime samples onto the plane or in the space described by different combination of two or three principal components, whereas a much sharper differentiation of the samples, regarding their origin and location, is observed when FPCAs are applied.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

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