Multivariate Statistical Analysis Techniques Research Articles

Determination of Factors Affecting Farmers' Preference for Corn Seed by Conjoint Analysis

Modeling consumer preferences among multi-attribute alternatives has been one of the main activities in consumer research. Conjoint analysis is a multivariate statistical analysis technique that reveals the acceptance of criteria such as yield, price and adaptability that are important in consumers' usage habits and preferences. In this study, it was aimed to determine the degree of importance given by grain corn growers to seed traits in corn seed purchase. For this purpose, conjoint analysis was applied and preferences in seed purchase were determined according to the data obtained from the survey conducted among farmers. The survey questions used in the research were applied to 123 farmers. Kendall's Tau value, which is the representativeness of the model, was 0.935 (p

Assessing drinking water quality in Eloued, south-East Algeria, using the groundwater pollution index (GPI) and the synthetic pollution index (SPI) model.

Groundwater quality degradation is a significant environmental issue worldwide, with potentially severe economic consequences and harm to ecosystems and biodiversity. This can directly affect human health, particularly in developing countries where rapid and uncontrolled urbanization is on the rise. Groundwater is the primary resource for meeting the water needs of the Eloued region, located in southeastern Algeria. Water is considered unfit for human consumption if its physico-chemical elements exceed national or international standards or guidelines. We used the GPI and SPI indices to evaluate the quality of groundwater suited for drinking. Groundwater samples were obtained from 22 wells at depths of more than 250m. Standard analytical procedures were used to determine the physicochemical characteristics of the collected samples, which included pH, EC, TDS, Na+, Ca+2, Mg+2, K+, Cl-, HCO3-, SO4-2, NO3-, NO2-, NH4+ and PO4-3. Multivariate statistical analysis and GIS techniques were used to process the results. The results of the selected physicochemical parameters were compared with World Health Organization (WHO) guidelines to determine the quality of drinking water. The findings indicate that the waters of the terminal complex aquifer are salty and contain medium to high quantities of main ions that surpass the established drinking water limits. The primary ions' relative abundance is Cl- > SO4-2 > HCO3- > NO3 for anions and Na+ > Ca+2 > Mg+2 > K+ for cations. Groundwater chemical types were dominated by Na+, Ca+2, Cl-, and SO4-2. Principal Component Analysis (PCA) showed that alteration and dissolution of carbonates, evaporates, salts, partly silicates, and evaporation, are the main reasons affecting the chemical composition of water in Eloued. The GPI results show that 18.18%, 54.54%, and 27.27% of the water samples were classed as lightly polluted, moderately polluted, or substantially polluted for drinking purposes, respectively. According to the SPI study, 9.09%, 36.36%, 36.36%, and 18.18% were considered drinkable, mildly contaminated, moderately polluted, and seriously polluted for drinking purposes, respectively. According to the GPI and SPI models' geographical distribution maps, potable water is generally scarce and concentrated in the northeastern section of the research area, near the town of Ourmes.

Experimental development of a method of short and medium-term photovoltaic generation forecasting using multivariate statistics and mathematical modeling

Uncertainties in photovoltaic solar energy production can make it challenging to dispatch energy into the electricity grid. Although photovoltaic generation storage solves this problem, a forecasting of the photovoltaic solar energy produced is necessary to control the energy injected into the grid. This article aims to develop the probabilistic methodology Reduced-Rank Regression (RRR) for forecasting photovoltaic generation in the short and medium terms. The RRR methodology forecasting uses the generation data of a grid-connected photovoltaic system. The proposed RRR model is simple, easy to access and apply, and does not use irradiance data. The model developed uses the multivariate statistical analysis technique. A advantage is that with a correlation with the performance indices of photovoltaic solar energy systems, the proposed method can be applied in any geographical location on the planet and with different photovoltaic solar energy systems. The application of the RRR methodology requires two searches/inputs. The first input is weather forecast data obtained from a weather forecasting platform, and the second is actual historical data on photovoltaic generation at the site where the method was developed. The proposed method was compared with the persistence method. Using a horizon of 1–10 h, the average monthly root mean square error for the RRR ranged from 7.3 % to 50.1 %. For the persistence method, the average monthly root mean square error ranged from 15.1 % to 65.0 %. Therefore, with the horizon of 24 h, the average monthly root mean square error for the RRR ranged from 4.5 % to 43.2 %. For the persistence method, the average monthly root mean square error ranged from 11.5 % to 75.0 %. We show experimentally that our method is competitive with the state-of-the-art in terms of obtaining photovoltaic generation forecasting without using solar radiation data.

Socioeconomic determinants of foodborne disease prevalence in Ecuador: A principal component analysis study

Foodborne diseases are a global health problem. Every year, millions of people die worldwide from these diseases. It has been determined that the high prevalence of these diseases is related to unfavorable socioeconomic conditions of the population. In this study, the relationship between foodborne diseases and socioeconomic conditions of the population was determined using principal component analysis as a multivariate statistical analysis technique. In this study, the socioeconomic variables of each Ecuador province and the prevalence of foodborne diseases (hepatitis A, salmonella, shigellosis and typhoid fever) during the years 2018 and 2019 were considered. The results show the relationship between foodborne diseases and the socioeconomic conditions of the population, as well as identifying regions more vulnerable to present high levels of prevalence of foodborne diseases, thus facilitating the implementation of social investment programs to reduce the prevalence of these diseases.

Assessment of groundwater quality using water quality index, multivariate statistical analysis and machine learning techniques in the vicinity of an open dumping yard

Assessment of groundwater quality using water quality index, multivariate statistical analysis and machine learning techniques in the vicinity of an open dumping yard

Insights into the usage of biobased organic acids for treating municipal solid waste incineration bottom ash towards metal removal and material recycling

The recycling of incineration bottom ash (IBA) is crucial for sustainable municipal solid waste (MSW) management and alleviating landfill burdens. However, limited environmentally friendly methods exist to effectively treat IBA for safe recycling. This study systematically examined the performance of biobased organic acids, namely citric acid (CA), oxalic acid (OA), lactic acid (LA), and levulinic acid (LEA), for IBA treatment. CA shows high extraction efficiency for most trace metals (e.g., Mn > 65 %, Pb > 50 %, Co approx.100 %, Cd > 85 %, Zn > 80 %, Ni > 80 %), while OA performs better for certain trace metals (e.g., Sn approx.99 %, Sb > 70 %, Mo > 70 %, Cr > 50 %). Multivariate statistical analysis and instrumental techniques were used to gain deeper insights into the critical mechanisms, including proton promoted dissolution, ligand related dissolution and precipitation/co-precipitation. The latter two mechanisms are distinctive metal extraction behaviours by organic acids compared to their inorganic counterparts. Both CA- and OA-treated alkaline-washed IBA residues demonstrate high leaching reduction efficiency (>99.9 %) of the critical heavy metals, making the treated IBA residues suitable for safe recycling as construction materials. This study highlights the potential of environmentally friendly organic acids which can be derived from bio-wastes for treating and repurposing IBA as resources for construction materials, promoting sustainable waste management for MSW incineration ash.

VAE-Based Interpretable Latent Variable Model for Process Monitoring.

Latent variable-based process monitoring (PM) models have been generously developed by shallow learning approaches, such as multivariate statistical analysis and kernel techniques. Owing to their explicit projection objectives, the extracted latent variables are usually meaningful and easily interpretable in mathematical terms. Recently, deep learning (DL) has been introduced to PM and has exhibited excellent performance because of its powerful presentation capability. However, its complex nonlinearity prevents it from being interpreted as human-friendly. It is a mystery how to design a proper network structure to achieve satisfactory PM performance for DL-based latent variable models (LVMs). In this article, a variational autoencoder-based interpretable LVM (VAE-ILVM) is developed for PM. Based on Taylor expansions, two propositions are proposed to guide the design of appropriate activation functions for VAE-ILVM, allowing nondisappearing fault impact terms contained in the generated monitoring metrics (MMs). During threshold learning, the sequence of counting that test statistics exceed the threshold is considered a martingale, a representative of weakly dependent stochastic processes. A de la Peña inequality is then adopted to learn a suitable threshold. Finally, two chemical examples verify the effectiveness of the proposed method. The use of de la Peña inequality significantly reduces the minimum required sample size for modeling.

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.

Study of Correlated Motions to Detect the Conformational Transitions of the Intrinsically Disordered Sheep Prion Peptide.

Intrinsically disordered proteins (IDPs) are known for their random structural changes throughout their sequence based on the environment. The mechanism underlying these structural changes is difficult to explain. All biological processes are known to follow the direction through which they act. A study of the correlated motion can help to understand the direction of the change. Herein, we introduced the multivariate statistical analysis (MSA) technique to study the correlated motion of the peptide. The correlated motion of the sheep prion peptide was studied with the change in the temperature and solvent. These techniques helped to identify the contributing residual motions that helped to form the different secondary structures of the protein and also the triggering factors that drive these sorts of residual motions. The structural details match the experimentally reported data. It was found that the direction of the change of the secondary structure for this peptide shifted from the C-terminal to the N-terminal with an increase in the temperature. It was found that the involvement of the hydrophobic residues present at the C-terminal and the middle residues (residues 12-17) is responsible for forming a β-sheet at the normal temperature. Hydration water was found to play an important role in this change. Insights gained from this study can be used to design strategies for desirable structural changes in the IDPs.

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

The Effect off Leadership Style and Work Environment on Online Team Performance Using SEM-PLS

&lt;p&gt;&lt;em&gt;The problem that occurs in the online group of Gen-Q Educational Product Distribution (EPD-R), namely the lack of qualified EPC which causes organizational performance in the EPD-R online group is less than optimal. The purpose of this study is to know whether the style of leadership and work environment affects the performance of the team in the EPD-R online group. The method used is SEM with the Tools Partial Least Square (pls). Partial Least Square (pls) is a multivariate statistical analysis technique by predicting the effect of variables X to Y variables and explaining theoretical relationships between the two variables. The results showed that the influence of leadership style variables on team performance was proven significant with a value of p-value 0.05 and the working environment variable on the team's performance did not have a significant effect because of the value of p-value 0.05. The coefficiency value of the leadership style variable on the performance of the team has a positive value. Where, when leadership style increases, team performance will also increase. While the coefficient value of the work environment variable on the performance of the team has a positive value.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;

Risk assessment and strontium isotopic tracing of potentially toxic metals in creek sediments around a uranium mine, China

The contamination of creek sediments near industrially nuclear dominated site presents significant environmental challenges, particularly in identifying and quantifying potentially toxic metal (loid)s (PTMs). This study aims to measure the extent of contamination and apportion related sources for nine PTMs in alpine creek sediments near a typical uranium tailing dam from China, including strontium (Sr), rubidium (Rb), manganese (Mn), lithium (Li), nickel (Ni), copper (Cu), vanadium (V), cadmium (Cd), zinc (Zn), using multivariate statistical approach and Sr isotopic compositions. The results show varying degrees of contamination in the sediments for some PTMs, i.e., Sr (16.1–39.6 mg/kg), Rb (171–675 mg/kg), Mn (224–2520 mg/kg), Li (11.6–78.8 mg/kg), Cd (0.31–1.38 mg/kg), and Zn (37.1–176 mg/kg). Multivariate statistical analyses indicate that Sr, Rb, Li, and Mn originated from the uranium tailing dam, while Cd and Zn were associated with abandoned agricultural activities, and Ni, Cu, and V were primarily linked to natural bedrock weathering. The Sr isotope fingerprint technique further suggests that 48.22–73.84% of Sr and associated PTMs in the sediments potentially derived from the uranium tailing dam. The combined use of multivariate statistical analysis and Sr isotopic fingerprint technique in alpine creek sediments enables more reliable insights into PTMs-induced pollution scenarios. The findings also offer unique perspectives for understanding and managing aqueous environments impacted by nuclear activities.

Unlocking solutions for cleaner waters: The Industrial Wastewater Quality Index in Egypt and beyond

AbstractThis research study conducted in Egypt focuses on addressing the issue of industrial water pollution and promoting sustainable water and environmental management practices. The study introduces a novel tool called the Industrial Wastewater Quality Index (IWQI) and utilizes two assessment methods to evaluate the extent of water pollution caused by industrial effluents. The IWQI takes into consideration both the environmental limits specified by Egyptian legislation and industry‐specific parameters, effectively condensing extensive water quality data into simplified categories. The IWQI model proves to be a dependable instrument for assessing the overall quality of groundwater intended for industrial use. It incorporates multiple water quality indices and employs multivariate statistical analysis techniques to determine the contribution of each parameter to industrial water quality. This comprehensive analysis enhances the reliability of the model, making it a valuable point of reference for similar locations worldwide, particularly those sharing similar climatic conditions. The outcomes of this study provide valuable insights for policymakers and stakeholders involved in regulating and mitigating industrial water pollution in Egypt. The findings also have relevance to other countries facing similar challenges, highlighting the importance of developing effective techniques for investigating pollution sources, monitoring water quality, and forecasting future trends. The study underscores the necessity of implementing proactive measures to address industrial water pollution and protect water resources and ecosystems. By emphasizing these aspects, the study emphasizes the importance of promoting sustainable water and environmental management practices. Overall, the IWQI and the study's findings contribute to the development of strategies to manage and control industrial water pollution and can guide policymakers and stakeholders in Egypt and other countries facing similar challenges.

A Data Driven Machine Learning Model for Fault Detection in Industrial Pipelines

Industry 4.0 has marked a paradigm shift in the way production and manufacturing operates. Process monitoring and fault diagnosis are important for the safety and reliability of industrial processes especially for smart manufacturing. As a data-driven process monitoring methodology, multivariate statistical analysis techniques, such machine learning based approaches have become extremely critical for automation. Pipelines carrying oil and gas are essential for a nation's economic sustainability. In order to maximise their function and prevent product losses during the transportation of petroleum products, they must be carefully inspected. However, they are susceptible to failure, which could have negative effects on the environment, financial loss, and safety. Therefore, evaluating the pipe's state and quality would be crucial. Despite being time-consuming and expensive, a number of inspection procedures are used to assure the safety of pipelines. However, due to the time consumption and error prone nature of manual inspection, data driven models are being explored for forecasting failure in oil and gas pipelines. The proposed work presents a Bayesian Regularized classifier for forecasting failure and the results show that the proposed approach outperforms the existing baseline techniques in terms of forecasting accuracy. Keywords:- Industry 4.0, oil pipelines; failure forecasting, Bayesian Regularization, Confusion Matrix, Classification Accuracy.

Assessment of heavy metals at mangrove ecosystem, applying multiple approaches using in-situ and remote sensing techniques, Red Sea, Egypt.

Mangrove areas are considered the most retention zone for heavy metal pollution as it work as an edge that aggregates land and sea sediments. This study aims to examine if the heavy metals' existence in the mangrove sediment is related to contamination or natural resources. In addition, it gives an interpretation of the origin of these metals along the Egyptian Red Sea coast. Twenty-two samples of mangrove sediments were collected and then, analyzed for metals (Mn, Ni, Cu, Fe, Cd, Ag, and Pb) using inductively coupled plasma mass spectroscopy (ICP-MS). Integration between the in-situ data, contamination indices, and remote sensing and geographical information science (GIS), and multivariate statistical analysis techniques (PCA) were analyzed to assess and clarify the spatial origin of heavy metals in sediment at a regional scale. The average concentration of heavy metals from mangrove sediments were shown to be substantially lower than the referenced value, ranging from moderate to significant except the levels of Ag were very high. The heavy metals concentrations were expected to be naturally origin rather than anthropogenic and that be confirmed by mapping of Red Sea alteration zones spots. These alteration zones are parallel to mangrove sites and rich by several mineralization types including heavy metals that are carried by flooding to the coastline. Remote sensing and GIS techniques successfully contributed to interpreting the pattern of the origin of heavy metals and discharging systems that control the heavy metals concentration along the Red Sea coast.

Multivariate Relationships Between Health Outcomes and Health System Performance Indicators: An Integrated Factor Analysis With Canonical Correlations

ObjectivesThis study aimed to investigate the relationships between sets of variables related to health system performance indicators and health outcomes. MethodsThe relationships between a set of health outcomes and a set of health system performance indicators of a developing country were examined using multivariate statistical analysis techniques. A combinative strategy of explanatory factor analysis and the canonical correlation coefficient was used to define linear structural relationships between study variables. Province-based data were gathered from2 official statistical records of the Turkish Statistical Institute for the year 2019. Life expectancy at birth, infant mortality rate, and crude death rate were accepted as health outcome indicators. ResultsThe explanatory factor analysis indicated 2 independent variable groups, namely (1) health-related human resources and capacity and (2) health service utilization characteristics. The results of the canonical correlation analysis illustrated good performance to define sparse linear combinations of the 2 groups of variables. There existed strong positive correlations between health outcomes and health-related human resources and capacity indicators (rc = 0.83; P < .001) and health service utilization indicators (rc = 0.59; P < .001). ConclusionsThe results of this study support the view that there is a linear and strong positive relationship between health outcomes and health-related human resources and capacity indicators. Further studies will combine big data analytics with multivariate statistical analysis techniques by studying large health system performance data sets.

Comparison of Classical and Robust Factor Analyses Methods

Factor analysis is a multivariate statistical analysis technique that has become very popular in recent years. In the factor analysis model, the error covariance matrix is assumed to be the multivariate normal distribution, and outliers are likely to be accounted for. Various estimation methods were compared with Monte Carlo simulation for the factor analysis model. The performances of the estimation methods were evaluated based on the ratio of the total variance explained and the criterion fit values. Considering the MLE, PCA, WLS, and GLS methods for classical factor analysis and the MCD, M, and S methods for robust factor analysis, the ratio of total variance explained, and fit values decreased as the sample size increased. When the number of variables increases, the ratio of total variance explained, and fit values increase at different sample sizes. It can be said that the WLS and GLS methods are better than others for classical factor analysis and the MCD and M methods are better than others for robust factor analysis.

Classification of Vietnamese Cashew Nut (Anacardium occidentale L.) Products Using Statistical Algorithms Based on ICP/MS Data: A Study of Food Categorization

Fingerprinting techniques, which utilize the unique chemical and physical properties of food samples, have emerged as a promising approach for food authentication and traceability. Recent studies have demonstrated significant advancements in food authentication through the use of fingerprinting methods, such as multivariate statistical analysis techniques applied to trace elements and isotope ratios. However, further research is required to optimize these methods and ensure their validity and reliability in real-world applications. In this study, the inductively coupled plasma mass spectrometry (ICP-MS) analytical method was employed to determine the content of 21 elements in 300 cashew nut (Anacardium occidentale L.) samples from 5 brands. Multivariate statistical methods, such as principal components analysis (PCA), were employed to analyze the data obtained and establish the provenance of the cashew nuts. While cashew nuts are widely marketed in many countries, no universal method has been utilized to differentiate the origin of these nuts. Our study represents the initial step in identifying the geographical origin of commercial cashew nuts marketed in Vietnam. The analysis showed significant differences in the means of 21 of the 40 analyzed elements among the cashew nut samples from the 5 brands, including 7Li, 11B, 24Mg, 27Al, 44Ca, 48Ti, 51V, 52Cr, 55Mn, 57Fe, 60Ni, 63Cu, 66Zn, 93Nb, 98Mo, 111Cd, 115In, 121Sb, 138Ba, 208Pb, and 209Bi. The PCA analysis indicated that the cashew nut samples can be accurately classified according to their original locations. This research serves as a prerequisite for future studies involving the combination of elemental composition analysis with statistical classification methods for the accurate establishment of cashew nut provenance, which involves the identification of key markers for the original discrimination of cashew nuts.

Assessment academic performance in online courses: a multivariate model

Grades and other student academic indicators are the most evaluated parameters in studies about teaching and learning. However, several authors point out that these indexes, alone, are not enough to explain the students' performance. To understand the predictors that facilitate or compromise learning in higher education, it is neces-sary to consider other variables related to students, teachers, and the teaching con-text. Thus, this paper presents a multivariate model for assessing student performance in online courses. The model was tested in the context of the COVID-19 pandemic with undergraduate students in Engineering. The course was Calculus 2. Data were analyzed using multivariate statistical analysis techniques. Among the 10 variables tested in the model. Three variables were significant and showed that the students' performance in Calculus 2 was impacted by: family income, cognitive and self-regulatory learning strategies, and teachers' instructional events. The main con-tribution of the study is the construction of a multivariate model that can be replicate in other contexts. In practices, professors and managers will have inputs to better plan the disciplines and avoid increasing retention and dropout rates.

Connecting Obsidian Artifacts with Their Sources Using Multivariate Statistical Analysis of LIBS Spectral Signatures

With the recent introduction of handheld instruments for field use, laser-induced breakdown spectroscopy (LIBS) is emerging as a practical technology for real-time in situ geochemical analysis in the field. LIBS is a form of optical emission spectroscopy that is simultaneously sensitive to all elements with a single laser shot so that a broadband LIBS spectrum can be considered a diagnostic geochemical fingerprint. Sets of LIBS spectra were collected for seven obsidian centers across north-central California, with data processed using multivariate statistical analysis and pattern recognition techniques. Although all obsidians exhibit similar bulk compositions, different regional obsidian sources were effectively discriminated via partial least squares discriminant analysis. Obsidian artifacts from seven archaeological sites were matched to their putative sources with a high degree of confidence.