Multivariate Statistical Techniques Research Articles

Television viewing and personality needs of South African adolescents

ACCORDING to the needs-and gratifications approach, a person actively chooses programmes, articles and films and uses the content of this mass media material to gratify certain needs in him or herself. In this paper, results of a longitudinal study of the relationship between certain personality needs on the one hand and the television viewing behaviour and television programme preferences of adolescents on the other hand, are discussed. Based on a large scale South African study of the influence of television on school-going children, the data of approximately 2 000 adolescents, retested from their 12th to 18th year, are used. Multivariate statistical techniques, e.g. profile analysis, were used to analyze the data obtained by various psychometric instruments and questionnaires.

Assessment of groundwater quality of Dehradun Valley, Uttarakhand, India, for drinking purposes using multivariate statistical techniques

Assessment of groundwater quality of Dehradun Valley, Uttarakhand, India, for drinking purposes using multivariate statistical techniques

Nursing diagnosis: Stress due to academic overload in high school students

Introduction: with the scientific and technological progress, the development of Information and Communication Technologies (ICT), the academic and social needs, the anxious world and the stress in the students, it is increasingly difficult and complicated to maintain a high performance. cognitive, psychic and optimal mental. Objective: to identify factors related to stress due to academic overload in first, second- and third-years high school students according to current regulations in Ecuador. Method: a methodology with a quantitative approach, with a descriptive scope, was applied. In addition, the Academic Stress Scale was used, which evaluates eight dimensions: Methodological deficiencies of the teaching staff, academic overload of the student, academic performance, interventions in public, exams, lack of value of the contents, participation difficulties. The questionnaire integrates 54 items; the response method integrates a 5-point Likert-type estimation scale. The instrument was applied to 110 students from the city of Latacunga in Ecuador. Multivariate statistical techniques such as principal component analysis and descriptive statistics were used to analyze sociodemographic data. Results: the results indicate that the factors related to academic stress are information management, the dimension of academic performance in students, exams and interventions in public, to which the students of the investigated educational institution are subjected. Conclusions: students face various difficulties to execute the demands of the tasks. They are closely related to academic and performance stress, including those linked to human subjectivity such as emotions, feelings and experiences

The spatial distribution of physicochemical parameters in coastal sediments along the Bay of Bengal Coastal Zone with statistical analysis.

The current study focused on the monitoring of pollution loads in the Kalpakkam coastal zone of India in terms of physico-chemical characteristics of sediment. The investigation took place at 12 sampling points around the Kalpakkam coastal zone for one year beginning from 2019. The seasonal change of nutrients in the sediment, such as nitrogen, phosphorus, potassium, total organic carbon, and particles size distribution, was calculated. Throughout the study period, the pH (7.55 to 8.99), EC (0.99 to 4.98 dS/m), nitrogen (21.74 to 58.12kg/ha), phosphorus (7.5 to 12.9kg/ha), potassium (218 to 399kg/ha), total organic carbon (0.11 to 0.88%), and particle size cumulative percent of sediments (from 9.01 to 9.39%) was observed. A number of multivariate statistical techniques were used to examine the changes in sediment quality. The population means were substantially different according to the three-way ANOVA test at the 0.05 level. Principal component analysis and cluster analysis showed a substantial association with all indicators throughout all seasons, implying contamination from both natural and anthropogenic causes. The ecosystem of the Kalpakkam coastal zone has been affected by nutrient contamination.

Explanation of major determinants of poverty using multivariate statistical approach and spatial technology: a case study on Birbhum district, West Bengal, India.

Poverty is not only the focal issue that has drawn worldwide attention but is also an essential issue in people's livelihoods. This research examines the primary factors of poverty in the Birbhum district. Multivariate statistical techniques have been used to identify the primary determinants. Ten parameters have been identified as significant drivers of poverty, six of which are physical, viz. slope, elevation, drainage density, pond frequency, soil texture, and rainfall. The remaining four sociocultural and economic parameters are literacy, major market center, population growth, and road density. A linear relationship has been established between the explanatory and response variables where the R-square or coefficient of determination value is 0.741, and this relationship explains more than 74% of the variables. The P-value of multi-linear regression is 0.000, which validates the model and permits the data for factor analysis to extract the major determinants. Factor analysis indicates that five essential factors have been found based on their eigenvalue viz., agro-climatic factor, infrastructural and educational factors, hydrological factor, demographic factor, and pedological factors. All the p-values of the correlation matrix are < 0.05, meaning all the relationships are valid and significant. This research also demonstrates the spatial analysis of data using GIS technology. The western part of the study area has been affected by the high influence of all factors due to the presence of plateau fringe and associated low productivity. The outcomes of the research are scientifically significant and this study helps the planners, higher authorities, and social workers to eradicate poverty from this region through formulating better policies and management.

Preprocessing of Spectroscopic Data Using Affine Transformations to Improve Pattern-Recognition Analysis: An Application to Prehistoric Lithic Tools

The analysis of spectral reflectance data is an important tool for obtaining relevant information about the mineral composition of objects and has been used for research in chemistry, geology, biology, archaeology, pharmacy, medicine, anthropology, and other disciplines. In archaeology, the use of spectroscopic data allows us to characterize and classify artifacts and ecofacts, to analyze patterns, and to study the exchange of materials, etc., as well as to explain some properties, such as color or post-depositional processes. The spectroscopic data are of the so-called “big data” type and must be analyzed using multivariate statistical techniques, usually principal component analysis and cluster analysis. Although there are different transformations of the raw data, in this paper, we propose preprocessing by means of an affine transformation. From a mathematical point of view, this process modifies the values of reflectance for each spectral signature scaling them into a [0, 1] interval using minimum and maximum values of reflectance, thus highlighting the features of spectral curves. This method optimizes the characteristics of amplitude and shape, reduces the influence of noise, and improves results by highlighting relevant features as peaks and valleys that may remain hidden using the raw data. This methodology has been applied to a case study of prehistoric chert (flint) artifacts retrieved in archaeological excavations in the Andévalo area located in the Archaeological Museum of Huelva (Huelva, Andalusia). The use of transformed data considerably improves the results obtained with raw data, highlighting the peaks, valleys, and the shape of spectral signatures.

Water Quality Assessment and Characterization of Rivers in Pasir Gudang, Johor via Multivariate Statistical Techniques

In Pasir Gudang, an accelerated industry-based economy has caused a tremendous increase and diversity of water contamination. The application of multivariate statistical techniques can identify factors that influence water systems and is a valuable tool for managing water resources. Therefore, this study presents spatial evaluation and the elucidation of inordinate complex data for 32 parameters from 25 sampling points spanning 20 rivers across Pasir Gudang, summing up to 1500 observations between 2015-2019. Hierarchical cluster analysis with the K-means method grouped the rivers into two main clusters, i.e., proportionately low polluted rivers for Cluster 1 (C1) and high polluted rivers for Cluster 2 (C2), based on the similitude of water quality profiles. The discriminant analysis applied to the cluster resulted in a data reduction from 32 to 7 parameters (Cl, Cd, S, OG, temperature, BOD, and pH) with a 99.5% correct categorization in spatial analysis. Hence, element complexity was reduced to a few criteria accountable for large water quality differences between C1 and C2. The principal component analysis produced 6 and 7 principal components after rotation for C1 and C2, respectively, where total variance was 62.48% and 66.85%. In addition, several sub-clusters were identified; two from C1 and three from C2, based on the principal contributing components. These results show that the functionality of multivariate techniques can be effectively used to identify spatial water characteristics and pollution sources. The outcomes of this study may benefit legislators in managing rivers within Pasir Gudang.

Fish farming systems diversity and implications in the Republic of Benin: Fish farm types and their economic performance

AbstractFish farming is promoted in the Republic of Benin to meet the demands of fish consumption and increase exports to neighbouring countries. Targeting fish farm policy interventions to increase the efficiency of heterogeneous fish farming systems is a challenge. Farm type delineation allows for simplifying the diversity in fish farming systems. Multivariate statistical techniques combined with the Calinski and Harabasz pseudo F statistic and bootstrapping were applied to determine the realistic number of fish farm types. Four fish farm types were identified and characterised based on farm intensity, species diversification, and the management capacity of operators. Furthermore, profits and both labour and capital productivity increased continuously from the extensive fish farms to the semi‐intensive farms. They also vary widely within farm types across the country. These farm types may be supported by appropriate fish farming promotion policy interventions. Most importantly, training programs must be tailored to each farm type to strengthen the technical and managerial capacity of the fish farmers.

Spatial patterns in water quality and source apportionment in a typical cascade development river southwestern China using PMF modeling and multivariate statistical techniques

River cascade development is one of the human activities that have the most significant impact on the water environment. However, the mechanism of cascade development affecting river hydrochemical components still needs to be further studied. In this study, water quality index(WQI), positive matrix factorization(PMF) model and multivariate statistical techniques were used to identify the mechanism of cascade development affecting river hydrochemical components in an typical cascade development Rivers, Lancang River, China. The results showed that the water quality of Lancang River is relatively good due to less affected by human activity. The spatial variation of river hydrochemistry is affected by the development of cascade reservoirs, and shows three patterns: irregular variation (pH and DO), fluctuating decreasing (Na+, Cl−, SO42− and HCO3−) and multi-peak variation (TN, TDN, NO3−-N and NH4+-N). It's worth noting that the concentration of the most hydrochemical parameters is higher in the upper reaches (less human activities) than that in the middle and lower reaches of river due to the retention effect of the reservoir on the chemical composition. The PMF model outputs revealed that the rock weathering and internal source, sewage and soil nitrogen, and chemical fertilizer were primary material sources of Lancang River. Compared with the natural channel zone (41.0%), the interaction of water-rock has more influence on chemical component in the reservoir area (56.3%), while the contribution of fertilizer (11.2%) to the river hydrochemistry is less. The sites of downstream of the reservoir dam were affected by the retention of the reservoir and the disturbance of the bottom drainage, which leads to the weakening of the influence of the sewage (44.7%) on the river material and the increase of the contribution of fertilizer (25.0%). These results could provide valuable information in controlling the eutrophication of cascade reservoirs and the scientific construction of river cascade reservoirs.

Multivariate Statistical Analysis of Hydrochemical and Microbiological Natural Tracers as a Tool for Understanding Karst Hydrodynamics (The Unica Springs, SW Slovenia)

AbstractVarious multivariate statistical techniques (MST) can provide valuable insights into water quality variability. Despite numerous studies in which these methods have been used, their potential has not been fully exploited. This paper presents an improved approach to better understand the hydrodynamics of karst systems. The integrated application of hierarchical cluster and principal component analysis in combination with factor analysis allowed the construction of an advanced multivariate chemograph. The analytical procedure was applied in a binary karst aquifer known for its complex hydrodynamics and mixing of water with similar hydrochemical composition. In addition, the study area provides access to an integral groundwater flow system (ponor‐cave‐spring) and offers extensive prior hydrogeological knowledge. The approach allowed reduction and discrimination of the main parameters affecting water quality characteristics. Their identification enabled recognition of three predominant recharge components: (a) stored water impact with Cl and electrical conductivity, (b) sinking stream impact with turbidity and bacteria composition and (c) karst aquifer impact with Ca/Mg ratio as principal parameters. The results supported innovative characterization of the dominant processes and isolation of temporal hydrodynamic phases of individual monitoring points within the aquifer system. On this basis, a spatio‐temporal conceptual model was developed and the hydrodynamic behavior of the main springs was revealed. The applied methodology demonstrated to be useful in ascertaining functioning of a complex karst system under flood event conditions.

Assessment of Surface Water Quality by using Multivariate Statistical Techniques of Various Lakes of Kancheepuram District in Tamil Nadu, India.

Multivariate statistical methods such as Principal Component Analysis/Factor analysis (PCA/FA), and Cluster Analysis (CA) were used to characterize spatial and temporal variations and to find out the pollution source of 11 lakes of the Kancheepuram district, a northeast district of Tamilnadu in India. Sampling events were conducted during the northeast monsoon (Dec 2015 &amp; 2016) and Pre-monsoon (Oct 2016). The 20 physicochemical water quality parameters were used to analyze the collected water samples.In PCA, four components were demonstrated up to 73.12% of the total variance in the data set. It clarifies that variations in water quality parameters concentration are mainly identified with agricultural, industrial, domestic wastes, and natural factors. The CA showed the formation of three clusters based on the water quality variation at different locations. From the CA data, sampling sites were classified into high, moderate, and least polluted. Among these sites, Kolavai, Nattapettai and Uthiramerur were highly polluted because of rapid urbanization and industrialization. Therefore, these areas need some effective measures to enhance the quality of water.

: Assessment of Surface Water Quality by using Multivariate Statistical Techniques of Various Lakes of Kancheepuram District in Tamil Nadu, India.

Multivariate statistical methods such as Principal Component Analysis/Factor analysis (PCA/FA), and Cluster Analysis (CA) were used to characterize spatial and temporal variations and to find out the pollution source of 11 lakes of the Kancheepuram district, a northeast district of Tamilnadu in India. Sampling events were conducted during the northeast monsoon (Dec 2015 &amp; 2016) and Pre-monsoon (Oct 2016). The 20 physicochemical water quality parameters were used to analyze the collected water samples.In PCA, four components were demonstrated up to 73.12% of the total variance in the data set. It clarifies that variations in water quality parameters concentration are mainly identified with agricultural, industrial, domestic wastes, and natural factors. The CA showed the formation of three clusters based on the water quality variation at different locations. From the CA data, sampling sites were classified into high, moderate, and least polluted. Among these sites, Kolavai, Nattapettai and Uthiramerur were highly polluted because of rapid urbanization and industrialization. Therefore, these areas need some effective measures to enhance the quality of water.

Assessment of Surface Water Quality by using Multivariate Statistical Techniques of Various Lakes of Kancheepuram District in Tamil Nadu, India.

Multivariate statistical methods such as Principal Component Analysis/Factor analysis (PCA/FA), and Cluster Analysis (CA) were used to characterize spatial and temporal variations and to find out the pollution source of 11 lakes of the Kancheepuram district, a northeast district of Tamilnadu in India. Sampling events were conducted during the northeast monsoon (Dec 2015 &amp; 2016) and Pre-monsoon (Oct 2016). The 20 physicochemical water quality parameters were used to analyze the collected water samples. In PCA, four components were demonstrated up to 73.12% of the total variance in the data set. It clarifies that variations in water quality parameters concentration are mainly identified with agricultural, industrial, domestic wastes, and natural factors. The CA showed the formation of three clusters based on the water quality variation at different locations. From the CA data, sampling sites were classified into high, moderate, and least polluted. Among these sites, Kolavai, Nattapettai and Uthiramerur were highly polluted because of rapid urbanization and industrialization. Therefore, these areas need some effective measures to enhance the quality of water.

Metabolome patterns identify active dechlorination in bioaugmentation consortium SDC-9™.

Ultra-high performance liquid chromatography-high-resolution mass spectrometry (UPHLC-HRMS) is used to discover and monitor single or sets of biomarkers informing about metabolic processes of interest. The technique can detect 1000's of molecules (i.e., metabolites) in a single instrument run and provide a measurement of the global metabolome, which could be a fingerprint of activity. Despite the power of this approach, technical challenges have hindered the effective use of metabolomics to interrogate microbial communities implicated in the removal of priority contaminants. Herein, our efforts to circumvent these challenges and apply this emerging systems biology technique to microbiomes relevant for contaminant biodegradation will be discussed. Chlorinated ethenes impact many contaminated sites, and detoxification can be achieved by organohalide-respiring bacteria, a process currently assessed by quantitative gene-centric tools (e.g., quantitative PCR). This laboratory study monitored the metabolome of the SDC-9™ bioaugmentation consortium during cis-1,2-dichloroethene (cDCE) conversion to vinyl chloride (VC) and nontoxic ethene. Untargeted metabolomics using an UHPLC-Orbitrap mass spectrometer and performed on SDC-9™ cultures at different stages of the reductive dechlorination process detected ~10,000 spectral features per sample arising from water-soluble molecules with both known and unknown structures. Multivariate statistical techniques including partial least squares-discriminate analysis (PLSDA) identified patterns of measurable spectral features (peak patterns) that correlated with dechlorination (in)activity, and ANOVA analyses identified 18 potential biomarkers for this process. Statistical clustering of samples with these 18 features identified dechlorination activity more reliably than clustering of samples based only on chlorinated ethene concentration and Dhc 16S rRNA gene abundance data, highlighting the potential value of metabolomic workflows as an innovative site assessment and bioremediation monitoring tool.

Investigation of zero-valent iron (ZVI)/H2O continuous processes using multivariate analysis and artificial neural networks

Multivariate statistical techniques and artificial neural networks (ANNs) were used for the analysis, interpretation, and modeling of the results obtained in the study of zero-valent iron (ZVI) reactive beds designed for contaminant removal. A wide range of operating conditions was evaluated through more than 120 rapid small-scale column tests (RSSCT). The production of Fe(II) and Fe(III) species, dissolved oxygen consumption, and pH variation along the reactive bed were used as response variables for evaluating the process performance. Due to the complexity of the system, and the difficulty in defining and fitting kinetic parameters, ANN models were used to simulate the system without the need for kinetic expressions. Therefore the latter were used for assessing the system behavior within the investigated experimental domain and for evaluating the relative importance of the operating factors. In addition, the application of the multivariate techniques cluster analysis (CA) and principal component analysis (PCA) revealed underlying relationships among the response variables. Moreover, although multiple physicochemical processes are involved, the results obtained through PCA indicate that the main trends can be rationalized by considering a few key reactions only. The strategy of analyzing RSSCT results with different numerical techniques provides valuable knowledge for designing real-scale ZVI-based treatments aimed at the efficient elimination of a wide range of contaminants in the aqueous phase.

Discriminating Geographical Origin of Cashew Nuts Based on the Combination of Multi-elements and Chemical Component Analysis

This study aims to distinguish Indonesian cashew nuts’ geographical origin based on their elemental profiles and chemical composition. Nine elements and five chemical components of cashew nut samples were analyzed. The profiles of four micro-elements, Zn, Cu, Mn, Cr, and five macro elements, K, Ca, Mg, Fe, Na, of Indonesian cashew nuts were determined using Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). Simultaneously, the chemical components of moisture, ash, total protein, total fat, and carbohydrate contents were measured using the AOAC standard. The combination of elemental profiles was then analyzed with a scatterplot matrix diagram and a multivariate statistical technique, Canonical Discriminant Analysis (CDA). Inter-region discrimination among four major Indonesian cashew nuts producers was achieved by applying CDA. Sodium (Na), calcium (Ca), potassium (K), manganese (Mn), zinc (Zn), and total protein were the best descriptors for cashew nuts origin. According to the findings, the most abundant element in cashew nuts is potassium (K), followed by magnesium (Mg), and calcium (Ca) (Ca). Fat is the most abundant chemical composition in cashew nuts at the same time. Potassium (K), magnesium (Mg), and calcium (Ca) concentrations showed major regional variations. On the other hand, zinc (Zn) and manganese (Mn) are two micro-elements that may help identify the origin of cashew nut samples. The use of a Canonical Discriminant Analysis scatters plot to visualize the origin of Indonesian cashew nut samples was less successful based on micro-element concentrations. The CDA scatter plot application based on macroelements, and the CDA scatter plot application based on a mixture of micro- and macro-elements produced better results than the CDA scatter plot application based on micro-elements. Furthermore, the best visual separation was achieved using a CDA scatter plot based on a combination of elemental profiles and protein concentration. For a complete characterization of cashew nuts, further research with a large number of samples is needed.

An assessment of water and sediment quality of aquatic ecosystems within South Africa’s largest floodplain

Ndumo Game Reserve (NGR), a Ramsar site, situated in South Africa’s largest floodplain (Phongolo River floodplain) in northern KwaZulu-Natal, with important aquatic ecosystems: the Usuthu and Phongolo rivers and their associated floodplain lakes. While the Phongolo River is regulated, the Usuthu River is unregulated and subjected to fewer anthropogenic activities that influence the downstream environmental quality of this Ramsar system. The present study evaluates the environmental quality of floodplain systems in the NGR. Water and sediment were sampled and analysed from both rivers and associated floodplain lakes during different hydrological periods (flow). Using multivariate statistical techniques, ionic compositions, as well as water and sediment quality indices of the ecosystems were evaluated. Key findings suggest spatial and flow-related differences, which highlight the human-driven impacts from the upper catchments and the rivers influence on their respective floodplain lakes. The environmental quality (water and sediments) could be classified as ‘good’ and predominantly unimpacted. Notably, ionic composition of river water showed the importance of the unregulated Usuthu River and its contribution to aquatic ecosystems within the NGR and their continued functioning. The quality indices further indicated that metals do not currently pose any ecological risks to these systems.

GC-TOF-MS-Based Non-Targeted Metabolomic Analysis of Differential Metabolites in Chinese Ultra-Long-Term Industrially Fermented Kohlrabi and Their Associated Metabolic Pathways.

Fermented kohlrabi is a very popular side dish in China. Chinese kohlrabies industrially fermented for 0 years (0Y), 5 years (5Y), and 10 years (10Y) were employed and analyzed by non-targeted metabolomics based on GC-TOF-MS, and the differential metabolites were screened using multivariate statistical analysis techniques, including principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA). The results showed that 47, 38, and 33 differential metabolites were identified in the three treatment groups of 0Y and 5Y (A1), 0Y and 10Y (A2), and 5Y and 10Y (A3), respectively (VIP > 1, p < 0.05). The metabolites were mainly carbohydrates, amino acids, and organic acids. Furthermore, 13 differential metabolites were screened from the three groups, including L-glutamic acid, L-aspartic acid, γ-aminobutyric acid, and other compounds. Four metabolic pathways termed alanine, aspartate, and glutamate metabolism, arginine biosynthesis, arginine and proline metabolism, and glycolysis/gluconeogenesis were the most significant pathways correlated with the differential metabolites, as analyzed according to the Kyoto Encyclopedia of Genes and Genomes (KEGG). The odors for the three ultra-long-term industrially fermented kohlrabies were significantly different, as detected by E-nose. The present work describes the changes in metabolites between different ultra-long-term industrially fermented kohlrabies and the associated metabolic pathways, providing a theoretical basis for the targeted regulation of characteristic metabolite biosynthesis in Chinese fermented kohlrabi.

Quantifying the contribution of dust sources in the arid area of northwest China using multivariate statistical techniques and Bayesian mixing models

In this study, topsoil samples and dust samples during dust storms were collected from the main dust source in northwest China, the geochemical elements were measured, and the contribution of dust sources was quantified using a discriminant function analysis (DFA) model and a MixSIAR model. Finally, the accuracy of the model was evaluated using an artificial mixture sample. The results showed that the dust sources and their contributions were significantly heterogeneous due to the natural and anthropogenic influences on the source-transport-sink process. DFA and MixSIAR models showed that the main dust sources in Dunhuang and Jiuquan were the Western Gobi and Western oasis, and Zhangye was mainly influenced by the Western Gobi, Western oasis, Middle Gobi, and Central oasis, with the influence of the Middle oasis being more pronounced. The Eastern Gobi, Eastern oasis, Badain Jaran desert, and Tengger desert have a greater influence on Jinchang, Wuwei, Baiyin, and Lanzhou. The contribution of each dust source in the Hexi region was found to show a significant distance effect. The accuracy of the models varied depending on the number of dust sources. The study provides new ideas for quantifying the contribution of dust source and for global dust storm control.

Implementation of Open Banking in Brazil

Goal: To provide an overview of the implementation of Open Banking in Brazil from the perspectives of financial sector professionals and specialists. Design / Methodology / Approach: A systematic literature review was conducted on three databases: Scopus, Web of Science, and BDTD. This led to the selection of 15 articles discussing the implementation of Open Banking in Brazil based on key variables from international literature. Questionnaires were used to investigate divergences and additions to the theoretical findings among a group of 150 specialists. The results obtained in the questionnaire were analyzed through the application of multivariate statistical techniques, in addition to qualitative techniques. Practical Implications: The main benefits and difficulties faced by countries that have already experienced Open Banking implementation were identified. The perception of respondents from different groups was investigated regarding the implementation of this system in Brazil and potential business models stemming from this phenomenon. Thus, this study offers an overview of Open Banking implementation in Brazil from the perspectives of financial sector specialists. Originality / Value: The overview of Open Banking implementation in the Brazilian banking sector is highly connected to investments in technology. This process has been disruptive, requiring banks to adopt a client-centric strategy instead of one based on products and services. As for the regulatory aspects, despite the country possessing a favorable environment for the development of new products and services stemming from Open Banking, banks are facing challenges to fulfill the implementation schedule established by the Brazilian Central Bank.