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Hierarchical clustering analysis (HCA) is a widely used unsupervised learning method. Limitations of HCA, however, include imposing an artificial hierarchy onto non-hierarchical data and fixed two-way mergers at every level. To address this, the current work describes a novel rootlets hierarchical principal component analysis (hPCA). This method extends typical hPCA using multivariate statistics to construct adaptive multiway mergers and Riemannian geometry to visualize nested dependencies. The rootlets hPCA algorithm and its projection onto the Poincaré disk are presented as examples of this extended framework. The algorithm constructs high-dimensional mergers using a single parameter, interpreted as a p-value. It decomposes a similarity matrix from GL(m, ℝ) using a sequence of rotations from SO(k), k << m. Analysis shows that the rootlets algorithm limits the number of distinct eigenvalues for any merger. Nested clusters of arbitrary size but equal correlations are constructed and merged using their leading principal components. The visualization method then maps elements of SO(k) onto a low-dimensional hyperbolic manifold, the Poincaré disk. Rootlets hPCA was validated using simulated datasets with known hierarchical structure, and a neuroimaging dataset with an unknown hierarchy. Experiments demonstrate that rootlets hPCA accurately reconstructs known hierarchies and, unlike HCA, does not impose a hierarchy on data. [ABSTRACT FROM AUTHOR]

This study investigates the application of Compositional Data Analysis (CoDA) and multivariate statistical techniques to geochemical data from the soils of the Campania region. The dataset examined includes 3571 soil samples analyzed for 37 chemical elements. Principal Component Analysis (PCA) was employed to reduce the dataset's dimensionality and identify key relationships between elements. The first PCA identified groups of highly correlated variables, which were then reduced to 20 representative elements for a second PCA. The three most significant principal components (PC1, PC2, and PC3) explained approximately 65% of the total variability. PC1 (accounting for 29.97% of variability) revealed an anticorrelation between Ti, La, and Sc with Au, Hg, and Ag, with positive scores primarily located in the inland Apennine areas. PC2 (21.8%) was dominated by Na, K, and Cu, with positive scores corresponding to volcanic deposits, aligning with the dispersion patterns of historical Vesuvian eruption products. PC3 (11%) was associated with Ca and S, with higher scores found in the alluvial plains and inland areas. These results demonstrate the efficacy of CoDA in minimizing spurious correlations and uncovering latent relationships between elements, thereby enhancing the interpretation of natural and anthropogenic processes influencing soil variability in the region. [ABSTRACT FROM AUTHOR]

We propose tabular two-dimensional correlation spectroscopy analysis for extracting features from multifaceted characterization data, essential for understanding material properties. This method visualizes similarities and phase lags in structural parameter changes through heatmaps, combining hierarchical clustering and asynchronous correlations. We applied the proposed method to data sets of carbon nanotube (CNT) films annealed at various temperatures and revealed the complexity of their hierarchical structures, which include elements such as voids, bundles, and amorphous carbon. Our analysis addresses the challenge of attempting to understand the sequence of structural changes, especially in multifaceted characterization data where 11 structural parameters derived from eight characterization methods interact with complex behavior. The results show how phase lags (asynchronous changes from stimuli), and parameter similarities can illuminate the sequence of structural changes in materials, providing insights into phenomena such as the removal of amorphous carbon and graphitization in annealed CNTs. This approach is beneficial even with limited data and holds promise for a wide range of material analyses, demonstrating its potential in elucidating complex material behaviors and properties. [ABSTRACT FROM AUTHOR]

The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas texture information was analyzed via the gray-level co-occurrence matrix (GLCM) and Law's texture feature analysis. The results revealed significant differences in color and texture among the samples. The fire–ice ion dimensionality reduction algorithm effectively fuses these features, enhancing their differentiation ability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the algorithm's effectiveness, with variable importance in projection analysis (VIP analysis) (VIP > 1, p < 0.05) highlighting significant differences, particularly for the fire value, which is a key factor. To further validate the reliability of the algorithm, Back Propagation Neural Network (BP), Support Vector Machine (SVM), Deep Belief Network (DBN), and Random Forest (RF) were used for reverse validation, and the accuracy of the training set and test set reached 98.83–100% and 95.89–99.32%, respectively. The method provides a simple, low-cost, and high-precision tool for the fast and nondestructive detection of food authenticity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In a world where efficiency and sustainability in poultry production are crucial, the need arises to find natural additives that enhance the growth of broiler chickens. Recent research has put ginger powder under the microscope, evaluating its impact as a growth promoter through a detailed analysis of plithogenic statistics. This study not only focuses on the quantitative aspects of weight gain and improved feed conversion, but also on the qualitative effects that this additive may have on the general health and well-being of the birds. The methodology used involves a rigorous and multifaceted approach, integrating biological and nutritional variables, which allows a deep and holistic understanding of the benefits of ginger powder in poultry farming. Preliminary results suggest that ginger powder could be a viable alternative to synthetic growth promoters, showing significant improvement in growth parameters of broilers. However, plithogenic analysis reveals complex nuances that require careful interpretation, as variations in bird response indicate that factors such as dosage and administration time are crucial to maximizing benefits. This finding opens a range of possibilities for future research and practical applications, pointing towards more natural and sustainable poultry production. Additionally, it raises important questions about the integration of herbal supplements into animal diets, inviting a broader debate about science and ethics in the food industry. [ABSTRACT FROM AUTHOR]

This research is carried out at the Educational Institution No. 35005 Reverend Father Bardo Bayerle of the Province of Oxapampa, Peru. We demonstrate that when there is a strong cultural identity, this means that the intercultural attitude of students is also strengthened. Cultural identity is a value that is currently being lost. This is a negative phenomenon, since with the reaffirmation of what one is culturally then one can consolidate the relationship with other groups. In this paper this phenomenon is studied from a statistical perspective on a survey carried out on students of this institution, some of them belonging to the target group and others belonging to the control group. To obtain more reliable results we apply Plithogenic Statistics, which is a generalization of Multivariate Statistics, where more than one random variable is studied simultaneously. Specifically, plithogenic statistics incorporates new components within the statistical study such as falsity or indeterminacy. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to investigate the genomic structure of the cattle breeds selected for meat and milk production and to identify selection signatures between them. Methods: A total of 391 animals genotyped at 41,258 SNPs and belonging to nine breeds were considered: Angus (N = 62), Charolais (46), Hereford (31), Limousin (44), and Piedmontese (24), clustered in the Meat group, and Brown Swiss (42), Holstein (63), Jersey (49), and Montbéliarde (30), clustered in the Milk group. The population stratification was analyzed by principal component analysis (PCA), whereas selection signatures were identified by univariate (Wright fixation index, FST) and multivariate (canonical discriminant analysis, CDA) approaches. Markers with FST values larger than three standard deviations from the chromosomal mean were considered interesting. Attention was focused on markers selected by both techniques. Results: A total of 10 SNPs located on seven different chromosomes (7, 10, 14, 16, 17, 18, and 24) were identified. Close to these SNPs (±250 kb), 165 QTL and 51 genes were found. The QTL were grouped in 45 different terms, of which three were significant (Bonferroni correction < 0.05): milk fat content, tenderness score, and length of productive life. Moreover, genes mainly associated with milk production, immunity and environmental adaptation, and reproduction were mapped close to the common SNPs. Conclusions: The results of the present study suggest that the combined use of univariate and multivariate approaches can help to better identify selection signatures due to directional selection. [ABSTRACT FROM AUTHOR]

The ancient city of Phaselis, which is located along gravel, coarse sandy, and sandy beaches, is a popular area visited by thousands of domestic and foreign tourists every year, and has been selected as the study area. Sediment samples collected from 57 different locations in the ancient city of Phaselis were analyzed using an X-ray fluorescence (XRF) spectrometer, and the major, trace, and rare earth element contents of the samples were revealed. The heavy metals arsenic (As), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), tin (Sn), stronsium (Sr), and zinc (Zn) were used in the pollution index calculations. Distribution maps revealed that heavy metal concentrations reached higher levels, especially in the eastern part of the study area. Therefore, it is recommended to plant rooted macrophytes that can absorb the heavy metals Cr and Ni and perform phytoremediation of the sediment in the region. [ABSTRACT FROM AUTHOR]

Copyright of Iranian Journal of Field Crop Science is the property of University of Tehran and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A short-term issue that has been occasionally investigated in the current literature is if (and, eventually, how) population dynamics (directly or indirectly) driven by COVID-19 pandemic have contributed to enlarge regional divides in specific demographic processes and dimensions. To verify this assumption, our study run an exploratory multivariate analysis of ten indicators representative of different demographic phenomena (fertility, mortality, nuptiality, internal and international migration) and the related population outcomes (natural balance, migration balance, total growth). We developed a descriptive analysis of the statistical distribution of the ten demographic indicators using eight metrics that assess formation (and consolidation) of spatial divides, controlling for shifts over time in both central tendency, dispersion, and distributional shape regimes. All indicators were made available over 20 years (2002–2021) at a relatively detailed spatial scale (107 NUTS-3 provinces) in Italy. COVID-19 pandemic exerted an impact on Italian population because of intrinsic (e.g. a particularly older population age structure compared with other advanced economies) and extrinsic (e.g. the early start of the pandemic spread compared with the neighboring European countries) factors. For such reasons, Italy may represent a sort of 'worst' demographic scenario for other countries affected by COVID-19 and the results of this empirical study can be informative when delineating policy measures (with both economic and social impact) able to mitigate the effect of pandemics on demographic balance and improve the adaptation capacity of local societies to future pandemic's crises. [ABSTRACT FROM AUTHOR]

Diverse multivariate statistics are powerful tools for musical analysis. A recent study identified relationships among different versions of the composition Sadhukarn from Thailand, Laos, and Cambodia using non-metric multidimensional scaling (NMDS) and cluster analysis. However, the datasets used for NMDS and cluster analysis require musical knowledge and complicated manual conversion of notations. This work aims to (i) evaluate a novel approach based on multivariate statistics of potential note degree of rhyme structure and pillar tone (Look Tok) for musical analysis of the 26 versions of the composition Sadhukarn from Thailand, Laos, and Cambodia; (ii) compare the multivariate results obtained by this novel approach and with the datasets from the published method using manual conversion; and (iii) investigate the impact of normalization on the results obtained by this new method. The result shows that the novel approach established in this study successfully identifies the 26 Sadhukarn versions according to their countries of origin. The results obtained by the novel approach of the full version were comparable to those obtained by the manual conversion approach. The normalization process causes the loss of identity and uniqueness. In conclusion, the novel approach based on the full version can be considered as a useful alternative approach for musical analysis based on multivariate statistics. In addition, it can be applied for other music genres, forms, and styles, as well as other musical instruments. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to evaluate the feasibility of two of the most prevalent agricultural residues in Ecuador, banana peels and pineapple peels, as a carbon source in the culture medium of Komagataeibacter hansenii for the production of bacterial cellulose (BC) and BC-based paper. This analysis includes an assessment of the productivity parameters of the obtained BC and the quality parameters of the BC-based paper, employing multivariate statistical methodologies. The experimental design consisted of fifteen treatments: T1 served as the control using the standard HS medium, while treatments T2–T8 used banana peel extracts (BPE), and treatments T9–T15 used pineapple peel extracts (PPE) at concentrations from 10% to 40% (v/v). Extracts were prepared with tailored pretreatments for each type of peel to optimize bioactive compound recovery. Standardized fermentation and purification conditions were applied, and once the cellulose was obtained, additives and coating agents were incorporated to produce paper samples from each treatment. The results indicated that higher BPE concentrations (T5, T6, T7, and T8) correlated significantly with increased Weight and Yield of BC, as well as improved grammage and water content in the BC-based paper. This highlights that efficient paper production is influenced by the quality of the bacterial cellulose used, with BPE-based media yielding optimal results due to their nutrient composition, which promotes bacterial growth and metabolic activity. This approach suggests a pathway for advancing sustainable and economical paper production. [ABSTRACT FROM AUTHOR]

The 1:100,000-scale Porang sheet in South Khorasan province is prone to skarn, massive sulfide, and sedimentary mineralization due to the presence of intermediate to ultrabasic volcanic and plutonic rocks and the variety of sedimentary rocks. This paper introduces the Principal Coordinate Analysis (PCoA) method. The PCoA method, along with the Principal Component Analysis (PCA) and Correspondence Analysis (CA) methods, has been used to identify the possible type of mineralization in the study area. Geological and mineralogical data and the analysis results of 25 elements from 314 stream sediment samples, taken from the study area, have been used for this purpose. The results of the data analysis show that the D1 coordinate, PC1 score, and location in the first cluster maps of the samples are most likely related to the mineralization in ultrabasic, basic, and listivinite rocks. After that, the D2 and D3 dimension maps, the PC2 and PC5 score maps, and the sample location map in the fifth cluster related to sedimentary rocks attribute the most probability to sedimentary mineralization, especially of Mn and Fe mineralization types, in the study area. Finally, there is the possibility of skarn and massive sulfide mineralization, whose locations can be predicted by the D4 dimension maps, the PC3 score map, and the sample location maps in second, third, and fourth clusters. Also, the comparison of data analysis results with two multivariate statistical methods shows that by choosing the number of dimensionality reductions, the principal components method can cover more variability than the principal dimensions method. While connecting the principal coordinate maps to the mineralization is easier and more reliable than the principal component score maps. Therefore, the proposal of this paper is the simultaneous use of PCoA and PCA methods to analyze geochemical data in an exploration region. [ABSTRACT FROM AUTHOR]

Knowledge of the technological properties of wood is important for its use and the quality of its products. The aim was to assess the quality of charcoal produced from wood grown in the Cerrado biome. Wood samples of Handroanthus roseoalba (ipê branco), Handroanthus heptaphyllus (ipê roxo) and Piptocarpha rotundifolia ('blackheart') were selected. Charcoals from these samples were produced at two charring temperatures (500 and 550 °C). Basic density, total extractives, lignin and holocellulose, gravimetric yield, higher calorific value and immediate chemical properties of the charcoals were determined. Near infrared (NIR) spectra were obtained on the transverse and longitudinal faces of the wood using a DLP® NIRscan™ portable spectrometer. Partial least squares regression was used to adjust predictive models of charcoal properties using the NIR spectra measured in the precursor wood. The coefficients of determination in the cross-validation (R2cv) ranged from 0.49 to 0.87 for volatile materials, from 0.48 to 0.88 for fixed carbon and from 0.49 to 0.85 for higher calorific value of charcoal. The spectra generated better models for properties of charcoals produced at 500 °C. The models generated were satisfactory, indicating that the NIR spectrometry can predict the quality of charcoal before it is produced. [ABSTRACT FROM AUTHOR]

For effective and sustainable water management, assessing the water quality and identifying potential sources that threaten the river system are crucial steps. In the present study, spatiotemporal variation of 20 hydrochemical variables, water quality indices, and multivariate statistics were applied to evaluate the quality of Yamuna River water. In the middle and lower stretch, the levels of electric conductivity (EC), total dissolved solids (TDS), turbidity, dissolved organic matter (DOM), chemical oxygen demand (COD), and nutrients were higher than in the upper stretch. Based on the trophic state index, the upper, middle, and lower stretches were mesotrophic, moderate, and low eutrophic in nature, respectively. In the drinking water category, the water quality index (WQI) ranged from almost good (upper stretch) to inappropriate (middle and lower stretch). Nemerow pollution index (PINemerow) and the comprehensive pollution index (CPI) indicated that most sites were strongly and moderately polluted, respectively. Various point and nonpoint sources of pollution deteriorated the quality of Yamuna water. Spatial cluster analysis divided eleven stations into three groups based on water variables similarity. Discriminate analysis indicated that water temperature, flow, turbidity, pH, dissolved oxygen (DO), magnesium hardness (Mg-H) and COD were the most influencing variables seasonally, while water flow, pH, chloride (Clˉ), DO, Mg-H, and nitrate–N were for spatial variation in Yamuna water quality. Five potential sources were identified using principal component analysis (PCA); anthropogenic, natural, agricultural non-point sources, metrological, and seasonal factors. This study emphasizes the importance of using multivariate statistical techniques to identify variability patterns and develop management plans to improve river water quality by identifying the key variables responsible for maximum deterioration. [ABSTRACT FROM AUTHOR]

Abstract The artisanal and small-scale mining (ASSM) sector has contributed to economies and is an integral component of the drivers of the gross domestic product (GDP) of most developing countries. To enhance the production of the ASSM sector, most gold (Au) endowed countries have supported the sector’s activities in diverse ways. However, despite this support, several factors, including poor exploration targeting, still affect the sector's production. This study, therefore, seeks to characterize the gold (Au) mineralization and its spatial distribution in the Singida region of the Tanzania Craton with a focus on identifying potential Au mineralized zones that could be demarcated and targeted for ASSM activities and further exploration exercises within the region. The study leverages stream sediment geochemical results to identify the elemental associations and pathfinder elements using multivariate statistics (Principal component analysis), multilinear regression modeling, and spatial distribution of Au and the pathfinder elements within the study area. The Au deposits in the area are strongly associated with the elements; Ni, Cr, V, Mg, Fe, Cu, and Al. Palladium (Pd), platinum (Pt), arsenic (As), and copper (Cu) are the main pathfinder elements in the area. Lead is not directly related to Au from the study. Mafic, ultramafic rocks, and clays are the most probable sources of Au in the area. Gold concentrations are focused on the southwestern fringes of the area. Southwestern, central south, and southeastern fringes of the area should also be explored considering the distribution of the dominant pathfinder elements. Alluvial and lateritic materials are also worth exploring.

Drinking water quality is a major concern in Quaternary sedimentary areas with unplanned urban growth and poor waste management, like Dili City, which relies heavily on groundwater to meet daily needs. This study assesses the suitability of groundwater for drinking by analyzing 113 samples from domestic wells, taking into account natural and human influences. The analysis focuses on major and minor ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, SO₄²⁻, HCO₃⁻, F⁻), physicochemical parameters (pH, electrical conductivity, total dissolved solids), and toxic elements (Mn, Fe, Pb, Al, As, Zn, Cd, Ni). Statistical methods (PCA and HCA) are used to determine the group of water types and trace chemical origins, while the Water Quality Index (WQI) assesses drinking suitability. Groundwater types identified include Ca-Cl-HCO₃, mixed Ca-Mg-HCO₃, Na-HCO₃, mixed Na-Cl-HCO₃, and Ca-HCO₃, with Ca-Mg-HCO₃ being the most common. The major ions are ordered as Ca > Na > Mg > K and HCO₃ > SO₄ > Cl. The Gibbs diagram shows the main geochemical processes are rock-water interactions, especially silicate and carbonate weathering. Organic matter and human activities also play a role. The WQI shows that 93.81% of the samples are “excellent” for drinking, while 2.65% and

Abstract Lithic artefacts provide the principal means to study cultural change in the deep human past. Tools and cores have been the focus of much prior research based on their perceived information content and cultural relevance. Unretouched flakes rarely attract comparable attention in archaeological studies, despite being the most abundant assemblage elements and featuring prominently in ethnographic and experimental work. Here, we examine the potential of flake morphology for tracing cultural change utilising 4,512 flakes, each characterised by 16 standard mixed-scale attributes, from a well-documented cultural sequence at the Middle Stone Age site of Sibhudu, South Africa. We quantified multivariate similarities among flakes using FLEXDIST, a highly versatile method capable of handling mixed, correlated, incomplete, and high-dimensional data. Our findings reveal a significant gradual change in flake morphology that aligns with the documented cultural succession at Sibhudu. Furthermore, our analysis provides new insights into the patterning of variability throughout the studied sequence. The demonstrated potential of flakes to track cultural change opens up additional avenues for comparative research due to their ubiquity, the availability of commonly recorded attributes, and especially in the absence of cores or tools. FLEXDIST, with its versatile applicability to complex lithic datasets, holds particular promise in this regard.

Diverse multivariate statistics are powerful tools for musical analysis. A recent study identified relationships among different versions of the composition Sadhukarn from Thailand, Laos, and Cambodia using non-metric multidimensional scaling (NMDS) and cluster analysis. However, the datasets used for NMDS and cluster analysis require musical knowledge and complicated manual conversion of notations. This work aims to (i) evaluate a novel approach based on multivariate statistics of potential note degree of rhyme structure and pillar tone (Look Tok) for musical analysis of the 26 versions of the composition Sadhukarn from Thailand, Laos, and Cambodia; (ii) compare the multivariate results obtained by this novel approach and with the datasets from the published method using manual conversion; and (iii) investigate the impact of normalization on the results obtained by this new method. The result shows that the novel approach established in this study successfully identifies the 26 Sadhukarn versions according to their countries of origin. The results obtained by the novel approach of the full version were comparable to those obtained by the manual conversion approach. The normalization process causes the loss of identity and uniqueness. In conclusion, the novel approach based on the full version can be considered as a useful alternative approach for musical analysis based on multivariate statistics. In addition, it can be applied for other music genres, forms, and styles, as well as other musical instruments.

Megalobrama amblycephala, which belongs to the genus Megalobrama of the subfamily Culterinae in the family Cyprinidae, is an important freshwater cultured species in China because of its low culture cost and high growth rate. Culter alburnus, belonging to the subfamily Culter, is another slender carnivorous species, whose meat is more delicate and delicious than that of M. amblycephala; however, the scales of C. alburnus are small and thin and are easily injured and fall off, and these fish are slow-growing and expensive to feed. Although both the fish are quite different in terms of diet, growth, and stress resistance, they are highly complementary to each other. Crossbreeding can combine the desired characteristics of both parents; however, this advantage is usually limited to the F1 generation. Theoretically, because the gonads are almost undeveloped during triploid growth and development, F1 heterosis can be maintained to avoid germplasm decline caused by continuous reproduction, and energy can be saved to accelerate growth and improve muscle quality. This will greatly expand the breeding space of new hybrid varieties and ensure high economic, social, and ecological value. Therefore, the triploids of M. amblycephala ♀ × C. alburnus ♂ have superior growth and economic value. To understand the growth and morphological characteristics of the triploids of M. amblycephala ♀ × C. alburnus ♂, the triploid population of M. amblycephala ♀ × C. alburnu ♂ was induced by hydrostatic pressure, and successfully induced triploid (hybrid-3n) and uninduced diploid (hybrid-2n) populations were obtained. At the same time, two self-breeding populations of Megalobrama (MA) and Culter (CA) were established. The growth performance and morphological characteristics of the four kinds of fish were compared and analyzed in growth contrast experiments. The results showed that during the 210-day growth cycle, the absolute weight gain (0.88±0.11 g/day) of hybrid-3n was 8.64% higher than that of hybrid-2n, 20.55% higher than that of MA, and 120.00% higher than that of CA. In terms of countable traits, the hybrid index of hybrid-3n and hybrid-2n was 41.05 and 36.07, respectively. In terms of measurable traits, the hybrid index of hybrid-3n and hybrid-2n was 36.73 and 57.57, respectively. Cluster analysis showed that hybrid-3n was first grouped with hybrid-2n, then grouped with maternal MA, and finally grouped with paternal CA. The results showed that hybrid-3n and hybrid-2n were closer to their mothers in quantifiable traits and frame structure, showing a maternal effect. Discriminant analysis showed that the discriminant accuracy of hybrid-3n was 90%, and the comprehensive discriminant rate of the four populations was 95%. A scatterplot of the discriminant analysis showed that the distribution centers of hybrid-3n and hybrid-2n were located between the parents and closer to the mothers. Hybrid-3n and hybrid-2n occupied partially overlapping areas, which indicates that the two are similar in proportion and frame structure, making it difficult to distinguish between them. These results are basically consistent with those obtained by cluster analysis. Most of the 11 biological traits used to construct the discriminant function were related to the longitudinal axis of the body, especially the ratio of the height of the body to the trunk, which is similar to the results obtained by principal component analysis. During principal component analysis, four principal components with a cumulative contribution rate of 75.10% were obtained, which mainly reflect morphological variations of body height and trunk length. In this study, the experimental fish were cultured in still water ponds with abundant bait. The morphological differences may be attributed to the adaptation of the fish to this ecological environment. Comprehensive analysis showed that: Hybrid-3n has a fast growth rate and has the basic conditions for promotion and application in production; The body size of hybrid-3n and hybrid-2n is between the parents, and both are slightly biased towards the mother, which mainly reflect the morphological variations of body height and trunk length; In this study, three multivariate analyses were used to effectively reflect the morphological differences among the four populations of hybrid-3n, hybrid-2n, MA, and CA from different perspectives, which has made them irreplaceable. In conclusion, this study confirmed the superior breeding potential of triploids of M. amblycephala ♀ × C. alburnu ♂, and provided basic data for the morphological comparison of hybrid offspring of M. amblycephala and C. alburnu, which is significant for the identification and protection of fish resources, and is expected to provide a theoretical basis for the establishment of new strains of M. amblycephala and C. alburnu. The results are of great significance for population identification and ploidy breeding of hybrid offspring of M. amblycephala and C. alburnu.

The branches and leaves of 8 varieties of Lonicera japonica as materials, a high-performance liquid chromatography detection method for 8 phenolic acid components was established to determine the content characteristics of phenolic acids in the samples. The content determination results were evaluated by multivariate statistical methods such as cluster analysis, factor comprehensive analysis, and partial least squares discriminant analysis (PLS-DA). The results indicated that the established method for determining the content of 8 phenolic acids in the branches and leaves of L. japonica was stable, reliable and simple. The variation range of phenolic acids total content in the branches and leaves of 8 varieties of L. japonica was 19.4162~33.6684 mg·g−1, and 40.9900~80.3068 mg·g−1, respectively. The total content of phenolic acids varied greatly among different varieties, with 'Beihua No.1' had the highest total content in both branches and leaves, and 'Juhua No.1' had the lowest total content. Cluster analysis found that among the branches of 'Beihua No.1' was clustered separately into one group, with higher content of each component than other varieties. Factor comprehensive analysis showed that the comprehensive score of 'Beihua No.1' was >1. PLS-DA analysis identified isochlorogenic acid A, isochlorogenic acid C, ferulic acid and chlorogenic acid as the main components that might cause differences in phenolic acid content in branches. Among leaves, 'Beihua No.1' and 'Jiufeng No.1' were clustered into one category, and their comprehensive factor analysis scores were both greater than 1. PLS-DA analysis identified chlorogenic acid, caffeic acid and ferulic acid as the main components that might cause differences in phenolic acid content in leaves. In summary, there were differences in the main phenolic acid composition characteristics of different varieties. In terms of phenolic acid content, the branches and leaves of 'Beihua No.1' and 'Jiufeng No.1' have more advantages. This study provides a scientific basis for the utilization of L. japonica branches and leaves.

Abstract Background Complexity in landscape structure is often assessed using individual metrics related to ecological processes. However, this rarely incorporates important relationships among metrics and may miss landscape structure effects. Multivariate statistics provide techniques for assessing overall landscape structure effects. We assessed how multivariate statistics could be used to connect landscape structure with an ecological process [bobcat (Lynx rufus) wildlife crossing structure (WCS) use]. We tested how landscape structure at WCS sites compared to the surrounding landscape and how structure affected detections at WCS sites. Our study was conducted in Cameron County, Texas, USA where WCSs are in various stages of construction and monitoring. We used a classified land use/land cover map and aerial LiDAR to calculate configuration and density metrics at WCS and random sites. We created indices for configuration and density using principal components analysis to assess landscape structure effects on camera trap detections at WCSs. Results Landscape structure at WCSs did not differ from random locations. Wildlife crossing structure use increased with greater woody cover and decreased with increasing vegetation density. Our indices allowed identification of differences in how configuration and density impacted WCS use. Ordination methods helped identify individual contributions of landscape metrics to the overall landscape structure effect. Conclusions Wildlife crossing structures are permanent fixtures on landscapes, so selecting appropriate locations using broad-scale landscape structure likely increases target species use. Using indices of landscape structure provides planners with a more holistic approach to WCS placement and provides a more comprehensive picture of landscape pattern and process relationships.

This study analyzed the water quality of the Yamuna river in Prayagraj across nine locations where the sampling was done upstream, downstream and middle stream from May 2019 to April 2020 using fourteen water quality parameters. Quarterly samples showed good Water Quality Index (WQI) values for summer (95.19), monsoon (77.28), and winter (90.77), but a decline in spring (102.11) due to religious activities was observed. Inductively coupled plasma mass spectroscopy suggested high concentration of Ca2+, Mg2+, K, and P in the river. Principal Component Analysis revealed 9 significant factors (eigen value >0.5) covering 25.13 – 93.89 % variance. Strong correlations included TDS-EC (0.853) and Ca2+ – Mg2+ (1.00) was observed. The correlation between water quality parameters generated by principal component analysis showed that the main parameters affecting the water quality vary in all the seasons. Based on the water quality indicators, anthropogenic activities are accountable to deteriorate the quality of river water. Therefore, the pollution status of the river need to be made publicly.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) mainly afflicting young women. Various steroids can influence the onset and development of the disease or, on the contrary, mitigate its course; however, a systematic review of steroidomic changes in MS patients is lacking. Based on the gas chromatography tandem mass spectrometry (GC-MS/MS) platform and, in the case of estradiol, also using immunoassay, this study performed a comprehensive steroidomic analysis in 25 female MS patients aged 39(32, 49) years compared to 15 female age-matched controls aged 38(31, 46) years. A significant trend towards higher ratios of conjugated steroids to their unconjugated counterparts was found in patients, which is of particular interest in terms of the balance between excitatory and inhibitory steroid modulators of ionotropic receptors. Patients showed altered metabolic pathway to cortisol with decreased conversion of pregnenolone to 17-hydroxypregnenolone and 17-hydroxypregnenolone to 17-hydroxyprogesterone and increased conversion of 17-hydroxypregnenolone to dehydroepiandrosterone (DHEA), resulting in lower levels of 17-hydroxyprogesterone, as well as indications of impaired conversion of 11-deoxy-steroids to 11β-hydroxy-steroids but reduced conversion of cortisol to cortisone. Due to over-activation of hypothalamic-pituitary-adrenal axis (HPAA), however, cortisol and cortisone levels were higher in patients with indications of depleted cortisol synthesizing enzymes. Patients showed lower conversion of DHEA to androstenedione, androstenedione to testosterone, androstenedione to estradiol in the major pathway, and testosterone to estradiol in the minor pathway for estradiol synthesis at increased conversion of androstenedione to testosterone. They also showed lower conversion of immunoprotective Δ5 androstanes to their more potent 7α/β-hydroxy metabolites and had lower circulating allopregnanolone and higher ratio 3β-hydroxy-steroids to their neuroprotective 3α-hydroxy-counterparts. [ABSTRACT FROM AUTHOR]

X-ray fluorescence (XRF) analyses are fast, clean, non-destructive, and compatible with on-field operations, which are some advantages over traditional determinations using coupled plasma optical emission spectroscopy (ICP-OES). The aim of this study was to advance in situ XRF approaches for assessing the nutritional status of soybean leaves (i.e. , P, S, K, Ca, Mn, Fe, Cu and Zn). More specifically, we propose a protocol to ensure accuracy of in-field analysis and then evaluate the predictive performance of XRF via different data modelling strategies for macro- and micronutrient determination. Therefore, the XRF sensor dwell time of 60 s and the maximum time of 5 min were determined for the analysis of the leaves after leaf abscission, taking into account the influence of moisture loss on the signal intensity of the lighter elements. Regarding the predictive performance of XRF data for nutrients determination, multiple linear regression (MLR) models resulted in lower root mean square errors (RMSE) for P (433 mg kg−1), S (204 mg kg−1) and K (1957 mg kg−1); Partial least squares regression (PLS) for Ca (519 mg kg−1); and simple linear regression (SLR) for Mn (9 mg kg−1), Fe (18 mg kg−1), Zn (5 mg kg−1). The different modelling strategies exhibited equivalent RMSE for Cu (2 mg kg−1). These prediction errors are within a ±20% range, demonstrating that the in situ protocols developed in this research are useful for predicting the nutrients concentration in soybean leaves. Our study shows the possibility of using the in situ XRF sensor for the rapid and practical nutrients determination in soybean leaves, presenting good potential as a crop diagnosis tool. • X-ray fluorescence: fast, clean, non-destructive tool for leaf nutrient analysis. • Protocols for soybean leaf nutrient analysis were evaluated. • X-rays fluorescence speed up foliar fertilizer decisions for soybeans. [ABSTRACT FROM AUTHOR]

Exploring the impact of performance audits on public company management becomes a crucial field of study, highlighting how these critical assessments not only reveal operational effectiveness but also shape strategic and policy decisions. Plithogenic statistics analysis, in particular, emerges as an innovative approach that goes beyond traditional methods, introducing the inherent complexity of multiple interdependent variables and their dynamic effects on organizational outcomes. This statistical framework not only captures the inherent fluctuations in the data, but also unravels the root causes of varying performances, providing deep insights that challenge static perceptions of public administration. From a practical perspective, plithogenic analysis not only quantifies current performance, but also anticipates future trends, equipping managers with powerful tools to adjust strategies and policies more precisely. By considering the complex interaction between multiple factors, from resource management to operational efficiency, this statistical approach allows for a more holistic and nuanced assessment of the impacts of performance audits. Thus, a dynamic landscape is revealed where each piece of data reflects not only superficial results, but also the hidden connections that define the effectiveness and long-term sustainability of modern public companies. [ABSTRACT FROM AUTHOR]

The exploration of the impact of educational reforms on teacher performance is a topic that has gained unusual relevance in recent decades. By analyzing these reforms through the prism of Plithogenic statistics, a window opens to a new approach that allows us to understand the inherent complexity of contemporary educational systems. Plithogenic statistics, which integrate heterogeneous and multivariate data, provide a powerful tool for unraveling how educational policies affect teacher performance. In this study, a multifaceted approach has been employed, ranging from longitudinal data analysis to qualitative evaluation of teacher perceptions, revealing an intricate interconnection between the reforms implemented and the results observed in the classrooms. The conclusions derived from this analysis are revealing and, in a certain sense, paradigmatic. Educational reforms, far from being mere administrative interventions, emerge as agents of dynamic change that influence the motivation, professional development and, ultimately, the performance of teachers. However, this impact is not homogeneous; varies significantly depending on the institutional context and the individual characteristics of educators. Through the application of Plithogenic methods, it has been possible to capture the subtle interaction of these factors, providing a more nuanced and holistic vision of educational reality. In summary, this study not only expands the understanding of educational reforms, but also highlights the importance of using advanced statistical approaches to capture the complexity of modern educational phenomena. [ABSTRACT FROM AUTHOR]

We propose a discrete random effects multinomial regression model to deal with estimation and inference issues in the case of categorical and hierarchical data. Random effects are assumed to follow a discrete distribution with an a priori unknown number of support points. For a K-categories response, the modelling identifies a latent structure at the highest level of grouping, where groups are clustered into subpopulations. This model does not assume the independence across random effects relative to different response categories, and this provides an improvement from the multinomial semi-parametric multilevel model previously proposed in the literature. Since the category-specific random effects arise from the same subjects, the independence assumption is seldom verified in real data. To evaluate the improvements provided by the proposed model, we reproduce simulation and case studies of the literature, highlighting the strength of the method in properly modelling the real data structure and the advantages that taking into account the data dependence structure offers. [ABSTRACT FROM AUTHOR]

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The exploration and development of resources and energy are fundamental to human survival and development, and geological drilling is a key method for deep resource and energy exploration. Intelligent monitoring technology can achieve anomaly detection, fault diagnosis, and fault prediction in the drilling process, which is crucial for ensuring production safety and improving drilling efficiency. The drilling process is characterized by complex geological conditions, variable working conditions, and low information value density, which pose a series of difficulties and challenges for intelligent monitoring. This paper reviews the research progress of the data-driven intelligent monitoring of geological drilling processes, focusing on the above difficulties and challenges. It mainly includes multivariate statistics, machine learning, and multi-model fusion. Multivariate statistical methods can effectively handle and analyze complex geological drilling data, while machine learning methods can efficiently extract key patterns and trends from a large amount of geological drilling data. Multi-model fusion methods, by combining the advantages of the first two methods, enhance the ability to handle complex multivariable and nonlinear problems. This review shows that existing research still faces problems such as limited data processing capabilities and insufficient model generalization capabilities. Improving the efficiency of data processing and the generalization capability of models may be the main research directions in the future. [ABSTRACT FROM AUTHOR]

Water quality monitoring is paramount in identifying and mitigating pollution sources, protecting aquatic ecosystems, and ensuring safe water for human and wildlife consumption. This study is aimed at evaluating the quality of drinking water sources in three communities located in a Sustainable Use Conservation Unit in the municipality of Mangaratiba, Rio de Janeiro, Brazil, employing a multivariate statistical analysis. A total of 161 water samples were collected from January to December 2022, encompassing 32 surface water and 129 tap water samples. Physicochemical parameters were determined in situ employing a Horiba U50 multiparameter probe. The samples were stored and transported at 4 °C to the laboratory for microbiological analyses concerning total coliforms and Escherichia coli using a commercial enzymatic test. All samples contained coliforms, while E. coli were detected in 87% of the samples. The multivariate analysis indicated that the microbiological water quality in sampling region R2 was influenced by rainy periods and that, in general, the water quality within R3 was the most affected by the transport of solids to the water sources. The statistical methods applied herein aided in characterizing the study areas and detecting points of attention regarding physicochemical and microbiological parameters that significantly influence the water quality of each sampling point. Representative points for each study region were identified and may be employed for future monitoring and prevention actions. [ABSTRACT FROM AUTHOR]

The occurrence of Potentially Toxic Elements (PTEs) and other chemical elements in urban and peri-urban soils impacts human health and quality of life, posing a challenge for geoscientists. This study investigated the soil geochemistry of Lamia City, focusing on identifying the geogenic and anthropogenic origins of elements. A total of 168 topsoil samples (0–10 cm) were collected in April 2023, and the analysis included the near-total concentrations of 51 elements. Descriptive, correlation, multivariate statistics (i.e., Factor Analysis-FA and Hierarchical Cluster Analysis-HCA), Geographic Information Systems (GIS) mapping, and mineralogical analysis were employed to identify potential element sources. The results indicated that the elements in soils originated from geogenic, anthropogenic, and mixed sources. Geogenic origins are associated with ultramafic rocks (e.g., Mg, Cr, Ni, Co, Fe, Sc, Mn), carbonate rocks (e.g., Ca, Sr), and Quaternary sediments (e.g., K, Na, Ba, Tl, Be, Rb, Ti, V, Ga, and Rare Earth Elements-REEs); associations are linked to specific identified minerals. All applied statistical analyses reveal that the mobility of chemical elements in the urban and peri-urban soils of Lamia city is primarily affected by geochemical processes such as element substitution, chemical weathering, pedogenesis, adsorption, precipitation, evaporation, and organic matter presence. The P, Ag, Hg, Pb, Sn, Zn, Sb, Cd, Cu, and U were associated with anthropogenic influences, particularly in areas with high population density, heavy vehicle traffic, and intensive agricultural practices. Additionally, some elements (e.g., Ca, Cd, Cu, Mo, Mn, and Li) exhibited mixed origins. This integrated approach offers valuable insights into the spatial distribution and sources of PTEs in urban and peri-urban environments, providing critical information for environmental management and public health protection strategies. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the spatiotemporal variation in water quality in the Grande River and the Ondas River, in the city of Barreiras, Bahia, Brazil. Water samples were collected at 11 points along the rivers, and eight physical–chemical parameters (electrical conductivity, pH, alkalinity, apparent and true color, turbidity, dissolved oxygen, and biochemical oxygen demand) and three microbiological indicators (heterotrophic bacteria, total and thermotolerant coliforms) were analyzed. Spatiotemporal variation was assessed using the multivariate techniques of principal component analysis/factorial analysis (PCA/FA) and hierarchical cluster analysis (HCA). The results of the PCA/FA highlighted eight of the eleven parameters as the main ones responsible for the variations in water quality, with the greatest increase in these parameters being observed in the rainy season, especially among the points influenced by sewage discharges and by the influence of the urban area. The CA grouped the results from 11 points into three main groups: group 1 corresponded to points influenced by sewage discharges; group 2 grouped points with mainly urban influences; and group 3 grouped points in rural areas. These groupings showed the negative influence of urbanization and also statistically significant variations between the groups and periods. The most degraded conditions were in group 1, and the least degraded conditions were in group 3. Assessment of the variations between the monitoring periods showed that rainfall had a significant impact on the increase or decrease in the parameters assessed, as a result of surface runoff linked to urbanization and increased river flow. [ABSTRACT FROM AUTHOR]