Multivariate Analysis Techniques Research Articles

Integrating Environmental Covariates into Adaptability and Stability Analyses: A Structural Equation Modeling Approach for Cotton Breeding

Breeding programs rely on genotype-by-environment interaction (GEI) to recommend cultivars for specific locations. GEI describes how different genotypes perform under varying environmental conditions. Several methods were proposed to assess adaptability and stability across environments. These methods utilize various statistical approaches like parametric and non-parametric regression, multivariate analysis techniques, and even Bayesian frameworks and artificial intelligence. The accessibility of environmental data through platforms like NASA POWER allows breeders to integrate this information into a breeding process. It has been done by using multi-omics integration models that combine data across various biological levels to create accurate predictive models. In the context of phenotypic adaptability and stability analysis, structural equation modeling (SEM) offers an interesting approach to integrating environmental covariates. This work aimed to propose a novel approach that integrates weather information into adaptability and stability analysis, combining SEM with the established Eberhart and Russell model. Additionally, a user-friendly applet, denoted ECERSEM-AdaptStab, was made available to perform the analysis. This approach utilized data from 12 cotton cultivar trials conducted across two growing seasons at 19 sites. This approach successfully integrated environmental covariates into a phenotypic adaptability and stability analysis of cotton cultivars. Specifically, the genotypes TMG 41 WS, IMA CV 690, DP 555 BGRR, BRS 286 and BRS 369 RF were recommended for favorable environments, while the genotypes TMG 43 WS, IMA 5675 B2RF, IMA 08 WS, NUOPAL, DELTA OPAL, BRS 335, and BRS 368 RF are more suitable for unfavorable environments.

Assessing Compliance with Indigenous Self-Determination Standards: A Comparative Analysis of Chile, Colombia, and Mexico

Recent advancements have been observed in the global legal framework, particularly in the recognition of Indigenous peoples’ rights, among which the right to self-determination stands out. This right encompasses the ability to define their political status and to pursue economic, social, and cultural development autonomously. The Inter-American Commission on Human Rights (IACHR) has identified six criteria to evaluate adherence to these rights. This study aims to assess the progress made by Chile, Colombia, and Mexico in meeting these criteria. Employing a non-experimental, cross-sectional, and exploratory research design, the study utilized a questionnaire based on a Likert scale to gather data from residents of Indigenous communities within the three countries under consideration to measure the perception that these individuals have regarding the compliance with the six standards of the IACHR, and specific questions have been designed based on the content of each standard. Once the degree of self-determination is understood, a multivariate analysis technique (Principal Component Analysis) will be used to compare the level of progress by country. The data collection instruments were applied in 2023 (September/November), with the results processed and the final report drafted in early 2024. Findings suggest that the criterion most adequately met is that of cultural identity and non-discrimination, whereas the criterion pertaining to the right to prior, free, and informed consultation and consent is the least fulfilled. Through principal component analysis, it was noted that Colombia exhibits a higher level of compliance with the right to self-determination, followed by Mexico, with Chile demonstrating a divergence and lagging in progress. The study concludes that a significant challenge for these countries lies in continuing efforts to effectively meet these standards and ensure the right to self-determination for Indigenous and tribal peoples.

Marker Peptides for Indicating the Spoilage of Milk-Sample Preparation and Chemometric Approaches for Yielding Potential Peptides in a Raw Milk Model.

The diminishing of food waste is gaining increasing importance, especially in context with a growing population and a need for the sustainable use of food resources. A more precise determination of the best-before date can contribute to this general aim. As proteoforms can be regarded as indicators for ecophysiological influences, their suitability for determining the spoilage and, consequently, the shelf-life of food is suggested. Proteoforms reflect the spoilage of food more accurately. The aim of the present study was to develop an efficient proteomics workflow to determine the shelf-life of milk as a prominent target. In this case, raw milk was chosen as model, as it degrades much faster. The integration of different multivariate analysis techniques was used to analyze the spoilage of raw milk with regard to aspects of its proteome. As the feasibility of such an approach has already been demonstrated in previous studies, it is further necessary to enable a robust and reproducible workflow, primarily gaining appropriate numbers and amounts of peptides when the research question differs and other dairy products are evaluated. In the present study, two approaches for gaining peptides were considered: In addition to a direct hydrolysis of a protein-rich sample solution, in-gel hydrolysis is another common approach in proteomics. By separating the proteins in a traditional gel electrophoresis before hydrolysis, the change in the individual proteins and, consequently, potential peptides can be monitored more specifically during storage. However, the traditional approach offers not only possibilities but also limitations that must be considered. The study showed that it is beneficial to apply a combination of different application strategies, as they complement each other and can thus increase the information content of a sample or confirm a theory. Mass spectrometric features, which represent a chemical-structural change of all kinds of compounds during storage, were selected, and three of them were identified as peptides, originating from α-s1-casein.

Influence of Individualism or Communitarianism of Actors on Opportunism in a Coopetition

This paper attempts to show that opportunism in a coopetitive market can be influenced not only by the individualism of the actors but also by communitarianism. Our theoretical anchor is the contract theory to explain the opportunism of the actors by communitarianism or individualism. We adopted a mixed methodology (quali-quanti). The data used are primary, from the administration of a questionnaire to 207 clandos in a coopetitive market in the alternative transport sector in Gabon. The multivariate qualitative comparative analysis technique (mvQCA) was used. The results indicate that opportunistic behavior in a coopetitive market is explained mainly by national communitarianism (Gabonese), then transnational communitarianism (natives of Central Africa except nationals and natives of West Africa), under conditions specific to each community; Opportunism is subsidiarily explained by the individualism of the actors who paradoxically trust each other in a competitive coopetition but these dishonest actors are paradoxically not sanctioned.

Attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) analysis of human nails: Implications for age determination in forensics.

A person's age estimation from biological evidence is a crucial aspect of forensic investigations, aiding in victim identification and criminal profiling. In this study, we present a novel approach of utilizing Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) spectroscopy to predict the age of donors based on nail samples. A diverse dataset comprising nails from donors spanning different age groups was analyzed using ATR FT-IR, with subsequent multivariate analysis techniques used for age prediction. The developed partial least squares regression (PLS-R) model demonstrated promising accuracy in age estimation, with a root mean square error of prediction (RMSEP) equal to 11.1 during external validation. Additionally, a partial least squares discriminant analysis (PLS-DA) classification model achieved high accuracy of 88% in classifying donors into younger and older age groups during external validation. This proof-of-concept study highlights the potential of ATR FT-IR spectroscopy as a non-destructive and efficient tool for age estimation in forensic investigations, offering a new approach to forensic analysis with practical implications.

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

PET Imaging of the Serotonin 1A Receptor in Major Depressive Disorder: Hierarchical Multivariate Analysis of [11C]WAY100635 Overcomes Outcome Measure Discrepancies

Abstract The serotonin 1A receptor has been linked to both the pathophysiology of major depressive disorder (MDD) and the antidepressant action of serotonin reuptake inhibitors. Most PET studies of the serotonin 1A receptor in MDD used the receptor antagonist radioligand, [carbonyl-11C]WAY100635; however the interpretation of the combined results has been contentious owing to reports of higher or lower binding in MDD with different outcome measures. The reasons for these divergent results originate from several sources, including properties of the radiotracer itself, which complicate its quantification and interpretation; as well as from previously reported differences between MDD and healthy volunteers in both reference tissue binding and plasma free fraction, which are typically assumed not to differ. Recently, we have developed two novel hierarchical multivariate methods which we validated for the quantification and analysis of [11C]WAY100635, which show better accuracy and inferential efficiency compared to standard analysis approaches. Importantly, these new methods should theoretically be more resilient to many of the factors thought to have caused the discrepancies observed in previous studies. We sought to apply these methods in the largest [11C]WAY100635 sample to date, consisting of 160 individuals, including 103 MDD patients, of whom 50 were not-recently-medicated and 53 were antidepressant-exposed, as well as 57 healthy volunteers. While the outcome measure discrepancies were substantial using conventional univariate analysis, our multivariate analysis techniques instead yielded highly consistent results across PET outcome measures and across pharmacokinetic models, with all approaches showing higher serotonin 1A autoreceptor binding potential in the raphe nuclei of not-recently-medicated MDD patients relative to both healthy volunteers and antidepressant-exposed MDD patients. Moreover, with the additional precision of estimates afforded by this approach, we can show that while binding is also higher in projection areas in this group, these group differences are approximately half of those in the raphe nuclei, which are statistically distinguishable from one another. These results are consistent with the biological role of the serotonin 1A autoreceptor in the raphe nuclei in regulating serotonin neuron firing and release, and with preclinical and clinical evidence of deficient serotonin activity in MDD due to over expression of autoreceptors resulting from genetic and/or epigenetic effects. These results are also consistent with downregulation of autoreceptors as a mechanism of action of selective serotonin reuptake inhibitors. In summary, the results using multivariate analysis approaches therefore demonstrate both face and convergent validity, and may serve to provide a resolution and consensus interpretation for the disparate results of previous studies examining the serotonin 1A receptor in MDD.

Alternative Tree Species for Sustainable Forest Management in the Brazilian Amazon

The scarcity of hardwoods from tropical forests makes the search for alternative species necessary for commercialization. This study aimed to establish groups of timber species from the Amazon Forest with potential for logging purposes through the assessment of their physical-mechanical properties, aiming to identify alternative species that can meet the market demands. We utilized data from the Forest Products Laboratory (LPF) (containing information on basic density and other wood mechanical properties) and the Timberflow platform, as well. We applied a multivariate cluster analysis technique with the aim of grouping species based on the technological characteristics of their wood and evaluating similarity among them to obtain homogeneous groups in terms of economic potential and utilization. The results indicated four homogeneous groups: Cluster 1 (40.72% of species, basic density-db: 690 kg m−3), Cluster 2 (13.92%, db: 260 and 520 kg m−3), Cluster 3 (27.32%, db: 550 and 830 kg m−3), and Cluster 4 (18.04%, db: 830 kg m−3). Most of the 20 listed species are classified as more commercially viable (70%), with high wood density. Species identified as alternatives include Dialium guianense and Zollernia paraensis for Dipteryx odorata, Terminalia argentea for Dinizia excelsa, Terminalia amazonia and Buchenavia grandis for Goupia glabra, and Protium altissimum and Maclura tinctoria for Hymenaea courbaril. The analysis highlighted the overexploitation of a restricted group of species and the need to find alternatives to ensure the sustainability of forest management. This study contributed to identifying species that can serve as alternatives to commercial ones, promoting a more balanced and sustainable forest management.

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.

Modified reconstruction approach after pancreaticoduodenectomy optimizes postoperative outcomes: Results from a multivariate cohort analysis.

Despite technical advances in recent decades and a decrease in hospital mortality (<5%), pancreaticoduodenectomy (PD) is still associated with major postoperative complications, even in high-volume centers. The present study aimed to assess the effect of a modified reconstruction technique on postoperative morbidity and mortality. A cohort study of all patients (n=218) undergoing PD between January 2010 and December 2019 was performed at Attikon University Hospital (Athens, Greece). Several variables were studied, including demographic data, past medical history, perioperative parameters, tumor markers and pathology, duration of hospitalization, postoperative complications, 30-day-survival, postoperative mortality and overall survival using multivariate logistic regression and survival analysis techniques. In this cohort, 123 patients [modified PD (mPD) group] underwent a modified reconstruction after a pylorus-preserving pancreaticoduodenectomy, which consisted of gastrojejunostomy and pancreaticojejunostomy on the same loop and an isolated hepaticojejunostomy on another loop. In the standard PD (StPD) group, 95 patients underwent standard reconstruction. The median age was 67 years, ranging from 25 to 89 years. Compared with in the StPD group, the mPD group had significantly lower rates of grade B and C pancreatic fistula (4.9% vs. 28.4%), delayed gastric emptying (7.3% vs. 42.1%), postoperative hemorrhage (3.3% vs. 20%), intensive care unit admission (8.1% vs. 18.9%), overall morbidity (Clavien-Dindo grade III-V: 14.7% vs. 42.0%), perioperative mortality (4.1% vs. 14.7%), and shorter hospitalization stay (11 days vs. 20 days). However, no difference was noted regarding median survival (35 months vs. 30 months). In this single-center series, a modified reconstruction after PD appears to be associated with improved postoperative outcomes. However, further evaluation in larger multi-center trials is required.

Heat stress tolerance in wheat seedling: Clustering genotypes and identifying key traits using multivariate analysis

Elevated atmospheric heat is considered as one of the bottlenecks for global wheat production. Screening potential wheat genotypes against heat stress and selecting some suitable indicators to assist in understanding thermotolerance could be crucial for sustaining wheat cultivation. Accordingly, 80 diverse bread wheat genotypes were evaluated in controlled lab condition by imposing a week-long heat stress (35/25 °C D/N) at the seedling stage. The response of heat stress was evaluated using multivariate analysis techniques on 20 morpho-physiological traits. Results showed significant variations in the studied traits due to the imposition of heat stress. Eleven seedling traits that contributed significantly to the genotypic variability were identified using principal component analysis (PCA). A substantial correlation between most of the selected seedling attributes was observed. Hierarchical cluster analysis identified three distinct clusters among the tested wheat genotypes. Cluster 1, consisting of 33 genotypes, exhibited the highest tolerance to heat stress, followed by Cluster 2 (18 genotypes) with moderate tolerance and Cluster 3 (29 genotypes) showing susceptibility. Linear discriminant analysis (LDA) approved that nearly 93 % of the wheat genotypes were appropriately ascribed to each cluster. The squared distance analysis confirmed the distinct nature of the clusters. Using multi-trait genotype-ideotype distance index (MGIDI), all 12 identified tolerant genotypes (BG-30, BD-468, BG-24, BD-9908, BG-32, BD-476, BD-594, BD-553, BD-488, BG-33, BD-495, and AS-10627) originated from Cluster 1. Selection gain in MGIDI analysis, broad-sense heritability, and multiple linear regression analysis together identified shoot and root dry and fresh weights, chlorophyll contents (a and total), shoot tissue water content, root-shoot dry weight ratio, and efficiency of photosystem II (PS II) as the most vital discriminatory factors explaining heat stress tolerance of 80 wheat genotypes. The identified genotypes with superior thermotolerance would offer resourceful genetic tools for breeders to improve wheat yield in warmer regions. The traits found to have greater contribution in explaining heat stress tolerance will be equally important in prioritizing future research endeavors.

Effects of the interaction between resilience development and digital transformation on the performance of the Australian food supply chains

ABSTRACT This study aims to investigate the relationship between resilience development and digital transformation (DT) and its effect on food supply chain (FSC) performance. A survey of 208 practitioners in the Australian FSC was conducted, and the collected data was analysed using multivariate data analysis techniques. Findings suggest a positive impact on performance, particularly in terms of on-time, in-full delivery and inventory turnover. Organisations with low resilience can significantly improve their performance through the adoption of technologies, while those with well-established resilience display a lesser impact. Our research enhances the current understanding of the relationship between DT and resilience, and its impact on the food supply chain during disruptive events such as the COVID-19 pandemic. Results can help practitioners to effectively implement both DT and resilience development to enhance performance, ensure coherent strategies and prevent frustrated efforts.

Continuous-Time Multivariate Analysis

The starting point for much of multivariate analysis (MVA) is an n × p data matrix whose n rows represent observations and whose p columns represent variables. Some multivariate datasets, however, may be best conceptualized not as n discrete p-variate observations, but as p curves or functions defined on a common time interval. Here we introduce a framework for extending techniques of multivariate analysis to such settings. The proposed continuous-time multivariate analysis (CTMVA) framework rests on the assumption that the curves can be represented as linear combinations of basis functions such as B-splines, as in the Ramsay-Silverman representation of functional data; but whereas functional data analysis extends MVA to the case of observations that are curves rather than vectors—heuristically, n × p data with p infinite—we are instead concerned with what happens when n is infinite. We present continuous-time extensions of the classical MVA methods of covariance and correlation estimation, principal component analysis, Fisher’s linear discriminant analysis, and k-means clustering. We show that CTMVA can improve on the performance of classical MVA, in particular for correlation estimation and clustering, and can be applied in some settings where classical MVA cannot, including variables observed at disparate time points. CTMVA is illustrated with a novel perspective on a well-known Canadian weather dataset, and with applications to datasets involving international development, brain signals, and air quality. The proposed methods are implemented in the publicly available R package ctmva. Supplementary materials for this article are available online.

Variation of surface solar radiation components from 2016 to 2020 in China: Perspective from geostationary satellite observation with a high spatiotemporal resolution

At present, traditional satellite datasets still grapple with inadequacies in terms of capturing solar radiation with fine spatiotemporal granularity. This study utilizes the high spatiotemporal resolution of CARE data, which is developed based on geostationary satellite observations, and employs multivariate analysis techniques to conduct an in-depth investigation into the multidimensional spatiotemporal variations of different types of solar radiation across various regions in China from 2016 to 2020. In addition, the potential of solar energy resources was also assessed using cluster analysis method. The results revealed an upward trend in different components of solar radiation across most of China, with shortwave radiation exhibiting a significantly negative correlation with PM2.5 concentrations (R = −0.91, p < 0.05). This finding suggests that the increase in SWR may be attributed to the effective implementation of China's Air Pollution Prevention and Control Action Plan. It suggests that China's endeavors to mitigate air pollution have not only resulted in improvements in national air quality but have also had an indirect positive effect on enhancing the potential for photovoltaic power generation. The assessment of solar energy resources potential indicated, with 99 % statistical confidence, that western China constitutes the core region for solar energy resources development, whereas northeastern and southeastern regions face certain constraints in solar energy resources utilization. Furthermore, we examined the specific influence of atmospheric circulation patterns on solar energy resources, uncovering that the El Niño phenomenon, by altering cloud distribution and variability, indirectly affects solar radiation intensity in specific regions. This study aids in understanding China's solar radiation variations, crucial for shaping effective energy policies towards carbon neutrality.

Urban-rural disparities and change in postnatal care use from 2016 to 2019 in Ethiopia: Multivariate decomposition analysis.

Postnatal care (PNC) is essential for early identification and management of life-threatening obstetric complications. Despite efforts by the Ethiopian government to improve maternal and child health service use, PNC service has remained low, and disparity across geographic locations is a major public health problem. This study aimed to investigate the change and contributing factors in PNC service use across geographical locations (rural-urban) and over time (2016 to 2019) in Ethiopia. We analyzed data on women who gave birth from the 2016 and 2019 Ethiopian Demographic and Health Surveys. A total of 6,413 weighted samples (4,308 in 2016 and 2,105 in 2019) were included in the analysis. A multivariate decomposition analysis technique was used to determine the change and identify factors that contributed to the change across geographical locations and over time. Statistical significance was defined at a 95% confidence interval with a p-value of less than 0.05. The prevalence of PNC use was higher among urban residents, and the urban-rural disparity reduced from 32.59% in 2016 to 19.08% in 2019. The difference in the composition of explanatory variables was the only statistically significant for the urban-rural disparity in PNC use in both surveys. Specifically, female household heads (4.51%), delivery at a health facility (83.45%), and birth order of two to three (5.53%) and four or more (-12.24%) in 2016 significantly contributed to the urban-rural gap. However, in 2019, middle wealth index (-14.66%), Muslim religion (3.84%), four or more antennal care contacts (18.29%), and delivery at a health facility (80.66%) significantly contributed to the urban-rural gap. PNC use increased from 16.61% in 2016 to 33.86% in 2019. About 60% of the explained change was due to the difference in the composition of explanatory variables. Particularly, urban residence (-5.79%), a rich wealth index (2.31%), Muslim (3.42%), and other (-2.76%) religions, having radio or television (1.49%), 1-3 (-1.13%), and 4 or more (11.09%) antenatal care contacts, and delivery at a health facility (47.98%) were statistically significant contributors to the observed change. The remaining 40% of the overall change was due to the difference in unknown behaviors (coefficient) of the population towards PNC. There was a significant change in PNC service use by residence location and over time in Ethiopia, with urban women in both surveys being more likely to use PNC service. The urban-rural disparity in PNC uptake was due to the difference in the composition of explanatory variables, whereas the change over time was due to the change in both the composition of explanatory variables and population behavior towards PNC. Increased antenatal care contacts and delivery at a health facility played a major role in explaining the gap in PNC services across residences and over time in Ethiopia, highlighting the importance of stepping up efforts to enhance their uptake in rural settings.

Designing a model for developing local business for rural women Iran with a sustainable development approach

This study endeavors to formulate a sustainable development-oriented model for home-based businesses catering to rural women in Guilan province. Employing a qualitative-quantitative research approach, the study is practical in its objectives and relies on survey methods for data collection. The research population encompasses two main groups: the first group involves academic professionals, scholars, and managers or experts from provincial organizations dedicated to employment and women’s issues, while the second group comprises 274 purposefully sampled individuals, including owners, managers, and women affiliated with micro-credit funds in Guilan province, selected through the snowball technique. Semi-structured interviews were conducted to gather data, and the Smart-PLS software package utilized the partial least squares (PLS) technique for advanced multivariate analysis in structural equation modeling. Evaluation of the home-based business development model for rural women, based on the coefficients of the direct path, reveals that educational (β = 0.112), social (β = 0.232), economic (β = 0.274), and policymaking (β = 0.381) variables significantly influence the sustainable development of home-based businesses (P < 0.05). Notably, the policymaking variable exerts the most substantial impact. Furthermore, the model designed for sustainable development can account for 71 percent of the variance in the dependent variable. It is recommended that the government should allocate financial resources specifically for rural women house-based businesses and policies should prioritize and actively support home-based businesses, recognizing their significant impact on rural women's economic development.

Contributions of computing in geosciences: a case study of geometallurgical forecasting

Geometallurgical modeling has been improved over the years with the incorporation of mineralogical, chemical, and metallurgical information. However, the diversity of the database brings challenges since the information is not unified in procedures, sample support, or quantities. Characteristics such as nonlinearity and nonadditivity of the geometallurgical database impose even more difficulties. The objective of this article is to demonstrate the feasibility of geometallurgical models from multivariate techniques combined with computational tools. The algorithms used in this study were developed in Python programming language. The multivariate analysis and machine learning techniques allowed the grouping of transitional typological domains and the quantification of uncertainty; the selection and reduction of the dimensionality of the heterotopic database; and the prediction of metallurgical behavior from exhaustive primary variables using a transfer function. The results were verified and proved to be satisfactory. Other benefits of the computational structure were: reduction of subjectivity; automation; and dynamic updates of models using new data.

Sedimentological, Geochemical, and Environmental Assessment in an Eastern Mediterranean, Stressed Coastal Setting: The Gialova Lagoon, SW Peloponnese, Greece

This study describes the prevalent sedimentological and geochemical patterns and investigates the environmental status of the bottom of Gialova lagoon, a highly vulnerable coastal site of the EU’s Natura 2000 network. For this task, lithological, geochemical, and microfaunal analyses of sediment samples were combined with a high-resolution bathymetric survey. Potential pollution was determined using geochemical-based (EF, I-geo, and PLI) and faunal (Foram-AMBI) indices. We find that sedimentation is mainly controlled by the bottom morphology, hydrodynamic variations, and biogenic productivity of the lagoon. The application of the multivariate factor analysis technique revealed four dominant factors explaining the geochemical processes occurring in the lagoon. The first factor, namely “terrigenous aluminosilicates associated with Corg vs. autochthonous biogenic carbonates”, discriminates the deposition of detrital sediments, related to the high adsorption of heavy metals—versus bioclastic sediments. The “sulfides” factor represents an anoxic phase of the lagoon floor, whereas the “Mn-oxyhydroxides” factor indicates increased manganese content with several compounded trace elements. The “phosphate” factor reveals multiple sources of phosphorus in the lagoon. The lagoon bottom shows negligible to minor contamination in heavy metals, except Mo and Pb, which induce moderate pollution levels. The maximum contamination and environmental stress concern two small-sized, shallow basins within the lagoon.

Hydrochemical characterization and evaluation of irrigation water quality using indexing approaches, multivariate analysis, and GIS techniques in K'sob Valley, Algeria

Irrigation plays a vital role in addressing increasing need for food production and promoting economic advancement. To meet the demands for food supply and economic progress, it is essential to underscore the significance of assessing water quality in dry regions. The current study was carried out to evaluate and predict the suitability of water quality for agricultural use in the K'sob valley in the M'sila region (Northeast Algeria). A combination of irrigation water quality indices (IWQIs), Geographic Information System (GIS) analysis and multivariate statistical methods were used for this purpose. Several physicochemical parameters, such as temperature (T°), hydrogen ion concentration (pH), electrical conductivity (EC), total dissolved solids (TDS), turbidity (Turb), chemical oxygen demand (COD), Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, SO42−, NO3−, NO2−, NH4+, PO4− and SiO22+ were all measured from 40 samples collected at ten surface water locations during four seasons. The concentrations of the main cation and anion were shown as follows: Na+>Ca2+> K+ > Mg2+, and SO42− > HCO3− > Cl− > NO3− indicating mixed Na-Cl-K or Na-SO4 water facies. Significant seasonal variation for each parameter (T, pH, Turbidity, Salinity, COD, NH4+, Cl−, SO4−, and NO2−) was reported (p < 0.05). Additionally, a significant spatial variation (p < 0.05) was observed among different stations for the parameters: TDS, EC, Ca2+, Na+, HCO3−, SO4−, NO3−, and PO43− (p < 0.05). The irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), Kelly index (KI), and permeability index (PI) had values varying between 28.1 and 56.8, 5.65 and 12.45, 75 and 87, 2.61 and 6.54 and 83, and 97, respectively, and a significant seasonal effect was recorded. According to the Wilcox diagram, 70% of samples were unsuitable for irrigation, while 30% of samples were questionable. The IWQI map revealed that 50% of the samples fell within the very poor category for irrigation, while 20% and 30% of the samples were inside the poor and unsuitable categories, respectively. The principal component analysis (PCA) and hierarchical cluster analysis (HCA) of K'SobValley water revealed three different categories of water based on elemental composition and seasonal variations. The results obtained in this study can be valuable for surface water management. Furthermore, the developed methodology can serve as a useful tool for identifying critical hydrogeochemical components in arid and semi-arid environments related to surface water.

Multivariate Analysis Techniques and Tolerance Indices for Detecting Bread Wheat Genotypes of Drought Tolerance

Drought stress is one of the biggest hardships in wheat cultivation because of the strong negative relationship between water deficit and crop yields owing to a lower grain weight, a shorter grain-filling period, a slower grain-filling rate, and reduced grain quality. Genotype–environment interaction (GEN:ENV) generates hardships in selecting wheat genotypes and ideotypes due to biased genetic estimates. Diverse strategies have been proposed to respond to the urgent need for concurrent improvements in yield performance and stability. This study’s purpose was to appraise genetic variation and GEN:ENV effects on yield and yield components to discover drought-stress-tolerant genotypes and ideotypes. This study evaluated 20 genotypes in three consecutive seasons under non-stressful and drought-stress conditions in a total of six ENVs. The broad-sense heritability ranged from 0.54 to 0.82 based on expected mean squares and ranged from 0.60 to 0.90 based on plot mean, but in the other three ways, it was usually greater than 0.90. The high values of (σgen:env2) revealed the effect that broad-sense heritability has on the expression of traits. G01, G03, G06, G07, G08, G10, G12, G13, G16, G17, and G18 were stable genotypes for grain yield (GY), according to additive main effects and a multiplicative interaction biplot for the six ENVs. Based on scores in the weighted average of absolute scores biplot (WAASB), G02, G04, G05, G08, G10, and G18 were selected as stable and high-performance for GY, and they were all selected as the best genotype groups using the WAASB-GY superiority index. From the results obtained from principal component analysis and hierarchical clustering and from the tolerance discrimination indices, G02, G04, G05, G18, and G19 are genotypes that produce a suitable yield under non-stressful and drought-stress conditions. In essence, combining approaches that take into consideration stability and high performance can contribute significantly to enhancing the reliability of recommendations for novel wheat genotypes.