Multivariate Statistical Techniques Research Articles

Understanding pesticide purchase practices and brand preferences among cotton growers in District Muzaffargarh, Punjab, Pakistan

In Pakistan, the pesticide use has increased over the years. Several companies market different pesticide brands and compete for capturing the attention of growers. This study was aimed at determining the pesticides buying behavior and brand preferences of cotton growers. Two leading cotton production districts including Multan and Muzaffargarh of South Punjab were selected. The study used quantitative research approach and data were collected from 160 cotton growers with the help of a survey questionnaire. The questionnaire included questions regarding farmers buying behavior and brand preferences towards pesticides. Data were analyzed using descriptive statistics and multivariate statistical techniques with the help of SPSS software. Results indicated that the major preferred brands included Bayer crop science, Syngenta, FMC Corporation and Arysta life science. Quality of the products and effectiveness were the major attributes that were given importance by cotton growers. Price of product, distance between farm and dealer shop, peer group advice, brand reputation, credit availability, and dealer suggestion were also given importance regarding purchase of pesticides. While discount and subsidy and advertisement were given less importance by the respondents. Results of this study can help pesticide firms in strategies formulation as well as product development to get attention of the cotton growers.

Efficient optimization of an accelerator neutron source for neutron capture therapy using genetic algorithms.

In recent years, genetic algorithms have been applied in the field of nuclear technology design, producing superior optimization results compared to traditional methods. They can be employed in the design and optimization of beam shaping assemblies (BSA) BSA to obtain the desired neutron beams. But it should be noted that the direct combination of Monte Carlo methods with genetic algorithms requires a significant amount of computational resources and time. Design and optimize BSA more efficiently to achieve neutron beams that meet specified recommendations. We propose an approach of NSGA II with crucial variables which are identified by multivariate statistical techniques. This approach significantly reduces the problem sizes, thus reducing the time required for optimization. We illustrate this methodology using the example of BSA design for AB-BNCT. The computational efficiency has tripled with crucial variables. By using NSGA II, we obtained optimized models conforming to both the new and old version IAEA BNCT guidelines through a single optimization process and subjected them to phantom analysis. The results demonstrate that models obtained through this method can meet the IAEA recommendations with deep advantage depth (AD) and high absorbed ratio (AR). The genetic algorithm with crucial variables displays tremendous potential in addressing BSA optimization challenges.

Water Quality Assessment Using the Water Quality Index, and Geographic Information Systems in Nador Canal, Morocco

In recent decades, water pollution has emerged as a significant concern, posing a threat to both humans and natural ecosystems. This study aimed to assess the spatial variations in water quality in the Nador Canal, Morocco using the water quality index combined with multivariate statistical techniques and geographic information systems (GIS). The parameters examined were then compared to the maximum permissible limit values recommended by Moroccan surface water standards and the World Health Organization. The results indicated that the WQI of Nador Canal is generally suitable for irrigation and unsuitable for drinking. It indicated that the quality of surface water in the Nador Canal is affected by organic pollutants, and this is evidenced by low levels of dissolved oxygen, the levels of elements were high, especially chlorine and sodium, in addition to a high concentration of ammonia, nitrate, and phosphate in some of the stations studied. Principal component analysis (PCA) results revealed that PC1–PC3 collectively accounted for 73% of the variation in surface water quality within the study area. The cluster analysis also proves that the water quality is relatively polluted. Potential contributing factors to surface water pollution include changes in the hydrological regime, household waste discharges, and agricultural activities. These findings furnish essential insights into water quality, particularly its suitability for irrigation, and contribute to enhancing the water quality management system of the Nador Canal in the Gharb region of Morocco.

Optimization of phosphorus wet acid extraction from sewage sludge ashes: Detailed process insight via multi-variate statistical techniques

To tackle the issue of phosphorus (P) supply, Sewage Sludge Ash (SSA) is a promising secondary source. The goal of this research is the optimization of the wet P extraction process in the view of providing valuable insights for the process scale-up. While most of the studies relied on the “one-variable-changed-at-a-time” approach, this work applied a statistical optimization model based on the Design of Experiments technique for investigating the influence of most relevant operating conditions of extraction process on Phosphorus Extraction Efficiency (PEE) with two types of SSA characterized by different levels of alkalinity. Experimental results highlighted the importance of ash characteristics, making some SSA not suitable for cost-effective phosphorus recovery due to the high acid dosage required in extraction process. Type of acid and contact time become significant factors only for Ca-rich ashes, especially when using H2SO4 as extracting agent. Optimal operating conditions (CH2SO4 = 1.3 N, L/S = 20, t = 2 h and CHCl = 2.0 N, L/S = 10, t = 2 h for Ash#1 and CH2SO4/HCl = 0.8 N, L/S = 10 for Ash#2) resulting in high PEE (80–94%) were identified for extraction performed with H2SO4 and HCl as a function of ash characteristics.

Multivariate statistical methods for analysis of physicochemical and microbiological parameters of well water from the village M’Pody

The pollution of surface water and groundwater is a real public health problem that is currently receiving particular attention throughout the world. The use of water for food or hygiene purposes requires excellent physicochemical and microbiological quality. Well water is used for many purposes by the inhabitants of M’pody, a village in the Anyama district of Côte d’Ivoire. In this village, an epidemic of diarrhoea was detected in January 2020. This epidemic claimed sixty-nine (69) victims. This study aims to evaluate well water quality controls using the methods of principal component analysis (PCA), correspondence factorial analysis (CFA), analysis of variance (ANOVA) and self-organizing map (SOM) algorithm. The parameters studied were, turbidity (Tur), conductivity (Cond), hydrogen potential (pH), temperature (T), nitrate (NO3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NO_{3^{-}}$$\\end{document}), nitrite (NO2-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NO_{2^{-}}$$\\end{document}), ammonium (NH4+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NH_{4^{+}}$$\\end{document}), phosphates (PO43-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PO_{4}^{3^{-}}$$\\end{document}), chlorides (Cl-)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(Cl^{-})$$\\end{document}, total hydrotimetric degree (DHT), sulfates (SO42-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SO_{4}^{2^{-}}$$\\end{document}), bicarbonate (HCO3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$HCO_{3^{-}}$$\\end{document}), total alkalinity contents (TAC), escherichia coli (E.coli), thermotolerant coliforms (CTH) and enterococcus faecalis (E.faecalis). Data were collected from seventy-two (72) wells in the village during four campaigns in 2020. Physicochemical parameters were determined by electrochemical and spectrophotometric methods. Microbiological analysis was carried out using membrane filtration technique. Descriptive statistics revealed that Tur, Cond, pH and T parameters did not meet world health organization (WHO, 2017) standards. However, the parameters NO3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NO_{3^{-}}$$\\end{document}, NO2-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NO_{2^{-}}$$\\end{document}, NH4+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NH_{4^{+}}$$\\end{document}, PO43-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PO_{4}^{3^{-}}$$\\end{document}, Cl-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Cl^{-}$$\\end{document}, DHT, TAC, SO42-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SO_{4}^{2^{-}}$$\\end{document} and HCO3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$HCO_{3^{-}}$$\\end{document} comply with WHO standards. The results of bacteriological analyzes confirm the presence in very significant numbers of indicators of faecal contamination (CTH, E.coli and E.faecalis) in all wells. The logical explanations for faecal pollution would come from the infiltration of septic tanks located near the wells and the run-off of waste water carrying human and animal faecal matter. The diarrhea epidemic would therefore be caused by the consumption of this polluted water. PCA, FCA and hierarchical cluster analysis (HCA) were jointly employed to identify the structure of wells and deduce the principal factors controlling the parameters of these well waters. ANOVA revealed the effect of human-induced activities as the main factors influencing the physicochemical and microbiological parameters of the studied well waters. Further studies should focus on multivariate statistical techniques for effective forecasting and monitoring of emerging pollution for improved water quality.

Characteristics and perceptions of project management in the productive sectors towards a sustainable future

Integrating sustainable approaches is imperative to align project management with the current demands and growing emphasis on environmental, social, and economic considerations. Despite that, the application of Sustainability faces challenges from two key aspects: the diversity of sustainability concepts, which adapt to various contexts, and the limited availability of methodologies to guide its adoption, monitoring, and implementation. This study aims to uncover perceptions related to project management and Sustainability by exploring the experiences, practices, and perceptions of expert professor-researchers of the Universidad Pedagógica y Tecnológica de Colombia. The focus is on productive sectors that significantly contribute to the gross domestic product. The methodological design involves developing and implementing a survey-type data collection instrument administered to 55 individuals. Internal consistency is assessed using Cronbach's alpha coefficient, and the results are analyzed using Principal Component Analysis, a multivariate statistical technique. The study's findings reveal critical impacts and existing connections, including the sustainability indicator and elements to consider for more suitable implementations in the short term within project management. Additionally, the complex relationships within sustainable considerations in project management are evident, theoretically supported but not consistently applied in the productive sectors. These contributions pave the way for future research, allowing the proposed instrument's replication in other groups and sectors interested in characterizing participants' conceptual performance regarding these elements.

INNOVATIVE TECHNOLOGICAL SOLUTIONS FOR REMOVAL OF PHARMACEUTICAL CONTAMINANTS FROM WATER SOURCES

The waste derived from pharmaceutical products represent a huge worldwide problem. The aim of this paper is to describe, analyze and compare some depollution procedures for such kind of contaminants and to highlight their advantages and disadvantages from various points of view in order to propose concrete cleaning solutions. Classical methods used for contaminant removal from wastewaters in wastewater treatment plants (WTP) and from drinkable water in treatment plants (TP) do not satisfactorily reduce the content of pharmaceutical pollutants (PhP). After an extended analysis of many factors which can be involved in the treatment efficiency, this paper proposes some rehabilitation solutions for existent TP and WTP by adding to the conventional technologies, some operations of advanced treatment. These advanced operations are filtration through semipermeable membranes, i.e., microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), advanced oxidation processes (AOPs), i.e., ozonation, H2O2, adsorption on activated carbon (AC). Statistical procedures are applied to compare these removal methods for such type of pollutants and to prove their efficacy. In this context, it is demonstrated that multivariate statistical techniques, combined with water quality indices represent a viable combination to perform accurate monitoring and spatiotemporal analysis of water quality assessment. This work presents concrete solutions for TP rehabilitation in order to comply with regulations in force related to PhP.

Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum)

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (FeSO4), zinc (II) sulphate (ZnSO4), and copper (II) sulphate (CuSO4), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

Preliminary study on the optical diagnosis of orbital rhabdomyosarcoma by Raman spectroscopy

To investigate the Raman spectral features of orbital rhabdomyosarcoma (ORMS) tissue and normal orbital tissue in vitro, and to explore the feasibility of Raman spectroscopy for the optical diagnosis of ORMS. 23 specimens of ORMS and 27 specimens of normal orbital tissue were obtained from resection surgery and measured in vitro using Raman spectroscopy coupled to a fiber optic probe. The important spectral differences between the tissue categories were exploited for tissue classification with the multivariate statistical techniques of principal component analysis (PCA) and linear discriminant analysis (LDA). Compared to normal tissue, the Raman peak intensities located at 1450 and 1655 cm−1 were significantly lower for ORMS (p < 0.05), while the peak intensities located at 721, 758, 1002, 1088, 1156, 1206, 1340, 1526 cm−1 were significantly higher (p < 0.05). Raman spectra differences between normal tissue and ORMS could be attributed to the changes in the relative amounts of biochemical components, such as nucleic acids, tryptophan, phenylalanine, carotenoid and lipids. The Raman spectroscopy technique together with PCA-LDA modeling provides a diagnostic accuracy of 90.0%, sensitivity of 91.3%, and specificity of 88.9% for ORMS identification. Significant differences in Raman peak intensities exist between normal orbital tissue and ORMS. This work demonstrated for the first time that the Raman spectroscopy associated with PCA-LDA diagnostic algorithms has promising potential for accurate, rapid and noninvasive optical diagnosis of ORMS at the molecular level.

Analytical solution to partial least squares

Partial least squares (PLS) is a widely used multivariate statistical technique, which can be used in neuroimaging, process monitoring, economics, etc. Because the standard PLS is trained by nonlinear iterative partial least squares (NIPALS) algorithm, which can only obtain the numerical solution rather than the analytical solution. Therefore, it is hard for the subsequent theoretical analysis in PLS and the iterative optimization process is computationally intensive. This paper proposes an analytical solution to take the place of NIPALS. This determination meaningfully contributes to future theoretical analysis of PLS. In addition, the analytical solution demonstrates that the selection of initial vector in NIPALS does not affect the PLS training results. Moreover, the analytical solution avoids the multiple iterative calculations, so the divergence of the calculation process will be avoided and the computation burden will be reduced significantly.

Using indirect survey techniques to investigate the relationship between employee personal experience of abusive supervision and job performance

PurposeTo propose the use of indirect survey protocols, in general and the item count technique (ICT), in particular, that ensure participant anonymity in organizations to explore the effect of employee perceived abusive supervision on job performance.Design/methodology/approachWe apply ICT to a sample of 363 employees (52.6% female) from Greek organizations. Utilizing multivariate statistical techniques, we investigated how employees assess the impact of their personal encounters with abusive supervision on job performance. This approach allowed us to explore the percentage of employees perceiving negative effects on job performance, distinguishing our study from previous studies that primarily focus on quantifying the extent or magnitude of abusive supervision in organizational settings. Also, we investigated how employee socio-demographic characteristics, human capital characteristics and affective traits relate to the evaluation of experienced abusive supervision as a negative factor for their job performance.FindingsWe found that approximately 62% of the respondents evaluated personal experience of abusive supervision as negatively affecting their job performance. We also found that the likelihood of employees evaluating personal experience of abusive supervision as having a negative impact on their job performance is: (1) higher for female employees, (2) does not depend on employee age, job tenure and education; (3) is lower for employees with managerial roles and (4) increases with employee trait negative affectivity.Originality/valueThe study is a response to the call for researchers to use innovative methods for advancing abusive supervision research. The study highlights the significance of taking a proactive stance towards addressing abusive supervision in the workplace, by using indirect survey methods that ensures employee anonymity. The results have implications for organizational strategies aimed at increasing awareness of abusive supervision and its impact on employee performance.

Evaluating the quality and nutritional content of bottled waters in Algeria

Abstract The present study offers a quality assessment of the mineral and spring waters marketed in Algeria within the national and international legislations, examines the potential contribution of bottled waters to essential elements intake and effects on public health based on empirical, graphical tools, multivariate statistical techniques and (DRI) system. The study covered a dataset of 30 mineral and 33 spring brands. The parameters included, from bottle labels, were of physicochemical nature. All brands comply with National WHO norms for the bottled waters, except for (Brand#63) in which NO2− exceeded the maximum permissible limit for mineral water and (Brands#4 and #21) in which TH and TDS exceeded the Algerian recommended guidelines for spring water. Nearly 5% of the total brands were of bicarbonate nature belonging to mineral water, while 25% of all brands were suitable for low-sodium-diet. PCA and HCA showed that bottled waters could be classified into two distinct groups, according to degree of mineralization. The DRI system revealed that Algerian bottled waters contributed substantially to the daily intake for Mg2+ with up to (63%), Na+ (40.36%) and Ca2+ (36%) for spring water for different ages and genders, whereas mineral water exceeded the maximum recommended daily intake for Ca2+ (128%) and Na+ (148.36%) for adults.

Mandarin Variety Significantly Affects the Metabolites Present in the Leaves

Late-season varieties of mandarin (Citrus reticulata Blanco) have a high economic value, so their study, characterization, and comparison among different commercial varieties is of great interest for agriculture. Detailed metabolomic analysis of mandarin leaves can provide valuable information on agronomic characteristics, vegetative development, and tree response to abiotic and biotic stresses. In this study, an analysis of the main metabolites presents in the leaves of three late-season mandarin orange varieties (‘Afourer’, ‘Orri’ and ‘Tango’), cultivated under homogeneous conditions, was carried out using nuclear magnetic resonance (1H NMR) and multivariate statistical analysis techniques. The results show that organic acids and sugars are the metabolites with the highest presence in mandarin leaves, especially malate and sucrose. Ten amino acids and other metabolites such as choline and trigonelline were also detected. Metabolites such as asparagine and isoleucine were widely implicated in the metabolic pathways of the detected compounds. The ‘Orri’ variety showed significantly more differences in metabolite concentrations compared to the other two varieties studied. Malate and sucrose were shown to be the metabolites with the greatest significant differences between the varieties compared. From an agronomic point of view, the ‘Orri’ variety differs from the other two varieties because it has concentrations of metabolites that provide good resistance to abiotic and biotic stresses and fruits of higher quality and sweetness.

Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater

The objective of the present study was to identify the most influent parameters in the composition of groundwater in the municipality of Icapuí, Ceará - Brazil, seeking correlations with the composition of the percolating aquifer formations that can be associated with the sources of these components. For this purpose, multivariate statistical techniques were applied by means of a Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). The PCA allowed a reduction of physicochemical parameters and determined the two components responsible for approximately 86% of total variance in the data for both sampling periods (rainy and dry). The first component is represented by variables that indicate natural rock weathering processes, and the second comprises seasonality and pollution indicators. Samples were also correlated through HCA according to compositional similarities, which were associated with possible natural or human sources.

Development of a hard cider flavor wheel using free word sorting and multivariate statistical techniques

AbstractFlavor wheels are visual tools built from standardized sensory lexicons that are used in many different industries to improve communication, marketing, and quality discrimination among products. To date, flavor wheels have been developed for spices, coffee, wine, beer, and many other foods and beverages—yet no flavor wheel has been constructed for hard cider. While there is no single established method for constructed flavor wheels, most approaches are based on sensory descriptive analysis (DA) and free word sorting activities that investigate the semantic similarity and dissimilarity of descriptive terms. This research study utilized multiple DA studies for the generation of a sensory lexicon, followed by independent word sorting tasks with cider industry professionals (N = 40) and untrained consumers (N = 58) to establish two flavor wheels that are broadly understandable to a variety of industry stakeholders. Based on the results of DISTATIS and additive tree partitioning, this research showcases a workflow for developing and refining flavor wheels that incorporate both stakeholder and researcher input and can be built upon by other analysts.Practical ApplicationsThis research demonstrates an accessible methodology for developing flavor wheels that incorporates input from diverse parties and considers the semantic associations of terms used by cider consumers and industry professionals. The proposed methodology is a useful framework for other sensory scientists seeking to efficiently develop flavor wheels for multiple audiences. As a demonstration, this research also delivers two flavor wheels—one that highlights discrepancies in how industry consumers and producers semantically understand sensory experiences compared to trained sensory personnel, and a second wheel that showcases how hard cider can more objectively be described. Together, these flavor wheels are tools for improving sensory communication, education, and marketing in the US hard cider industry.

Selection of chemical marker for Cnidii Rhizoma by Gyeongbuk production area and harvest time using UPLC-QTOF/MS and multivariate statistical analysis techniques

Selection of chemical marker for <i>Cnidii Rhizoma</i> by Gyeongbuk production area and harvest time using UPLC-QTOF/MS and multivariate statistical analysis techniques

Principal component analysis on the effect of early morning awakening in major depressive disorder

Sleep disturbance is one of the most prevalent symptoms associated with Major Depressive Disorder (MDD). A recent article (Wu et al., 2022, Journal of Multiscale Neuroscience 1, 133-139) explored the significant relationship between early morning awakening (EMA), a type of sleep disturbance, and recovery in MDD patients. In the paper, the authors examined the relationship between EMA and the treatment of MDD with twelve neuropsychological parameters. The authors employed two univariate statistical techniques, students’ t-test and ANOVA, to analyze their data. While their analysis derived a meaningful conclusion that EMA may result in a statistically and clinically significant delay in recovery, we found that a multivariate statistical technique, principal component analysis (PCA), extracted additional quantitative information from their study. In this paper, we present quantitative features in the interaction between EMA and the treatment of MDD obtained from PCA.

Risk identification of salts and heavy metals in water by multivariate statistical techniques and GIS based interpolation: A case study of Saeedabad, Sindh, Pakistan

Underground water is the most common source for drinking and depends on environmental functions in the ecosystem. In Pakistan water demand is increasing and quality of water is decreasing due to contaminations of salts and heavy metals. Total of 45 water samples were collected form Taluka Saeedabad, District Matiari, Sindh and evaluated for physicochemical parameters, anions, essential and trace metals. The study was examined for multivariate statistical integrated approaches along with conventional plots of geographic mapping employed to classify hydrological study of groundwater. The geochemical modelling using GIS map of the study area were drawn for TDS, F, As and WQI to classify groundwater quality. TDS results ranged from 245 to 1926 mg/L with mean 923.45 mg/L. TDS of 17 (38%) samples exceeded WHO limit and were not fit for drinking. Higher samples concentration range for anions and cations above the permissible limits of WHO were found in the order of HCO3− >NO3− >SO42− >PO43− >Cl− and Na+ >K+ >Mg2+ >Ca2+. Toxic elements of fluoride and arsenic were in 31% and 18% samples exceeding the WHO limit. The calculated values for WQI were found in the categories good (27%), poor (49%), very poor (20%) and unfit (4%) for drinking. The hydrochemical investigation in Piper diagram indicated similar characteristics with mixed type water. Gibbs diagram indicated most samples were found in evaporation dominancy and rock weathering dominancy for cations and anions. Health risk assessments are reported in hazard quotient, chronic daily intake and contamination index. Groundwater quality for irrigation were also analyzed and moderately suitable for agriculture.

Water Quality Assessment of Tigris River Using Multivariate Statistical Techniques

Factor and cluster analysis, two multivariate statistical approaches, have been used to analyze temporal and spatial variations and pollutants sources effecting the water quality of Tigris River from the north to the south of Baghdad city. From October 2021 to September 2022, 15 water quality parameters (electrical conductivity (EC), pH, water temperature (WT), turbidity (Tur), total hardness (TH), biochemical Ooxygen Ddemand (BOD), dissolved oxygen (DO), Mg+, Ca+, SO4, dissolved solids (TDS), total suspended solids (TSS), total chlorine (TC), Cr, and Al) were tested at five monitoring sites (Balad, Al-Ghrai’at, Al-Shuhada'a Bridge, Al-Za’franiya, and Jisr Diyala), along the study area of the river. Results revealed that the major factor that affects the water quality in the study area is the anthropogenic factor. Spatial cluster analysis of the monitoring sites revealed three different groups that were compared to one another where each site suggested a different degree of contamination in the Tigris river water quality.

The Implementation of Green Human Resource Management: A Survey on the Manufacturing Industry in Indonesia

This study investigates the implementation of Green Human Resource Management (GHRM) in the manufacturing industry in Indonesia, with a focus on achieving sustainable organizational performance. It addresses a research gap and develops conceptual models to advance sustainable development goals (SDGs), particularly in Indonesia, encompassing social, economic, educational, health, and environmental aspects. A survey method was employed, utilizing an associative quantitative approach with multivariate statistical techniques, specifically structural equation modeling partial least squares (SEM-PLS). The study involved 75 manufacturing enterprises in Indonesia's Karawang industrial district, with Managers or Supervisors serving as respondents. Data was collected using a questionnaire as the primary measurement instrument. The research falls under TKT 2 in terms of technology readiness, involving the formulation of a research model concept. Key findings suggest that the implementation of GHRM practices in the manufacturing industry positively impacts Green Intellectual Capital and contributes to sustainable organizational performance. Furthermore, the study underscores the importance of GHRM in enhancing environmental sustainability and organizational competitiveness within the Indonesian manufacturing sector.