Statistical Discriminant Analysis Research Articles

Identification of dental sexual dimorphism in deciduous teeth: A cross sectional study

The study of sex diagnosis is essential to reconstruct the biological profile of an unknown individual. Two approaches are often used: morphometric and scopic. This three-dimensional morphometric study aimed to assess sexual dimorphism in temporary dentition.: A total of 200 dental impressions digitized in 3D using the Revopoint® POP2 3D scanner were used for this odontometric analysis. Discriminant statistical analysis (Student's t-test) revealed that boys had higher mean values than girls for all distances measured. A highly significant sexual dimorphism was observed in the temporary second molars. The distance between the mandibular canines was slightly greater in boys, although this difference was not statistically significant. In conclusion, odontometric parameters are a simple, reliable, and inexpensive method of sex determination. The temporary canines and second molars are the most dimorphic.

Effective Descriptor for Screening Single-Molecule Conductance Switches.

The adsorption-type molecular switch exhibits bistable states with an equivalently long lifetime at the organic/inorganic interface, promising reliable switching behavior and superior assembly ability in the electronic circuits at the molecular scale. However, the number of reported adsorption-type molecular switches is currently less than 10, and exploring these molecular switches poses a formidable challenge due to the intricate interplay occurring at the interface. To address this challenge, we have developed a model enabling the identification of diverse molecular switches on metal surfaces based on easily accessible physical characteristics. These characteristics primarily include the metal valency electron concentration, the work function of metal surfaces, and the electronegativity difference of molecules. Using this model, we identified 56 new molecular switches. Employing the gradient descent algorithm and statistical linear discriminant analysis, we constructed an explicit descriptor that establishes a relationship between the interfacial structure and chemical environment and the stability of molecular switches. The model's accuracy was validated through density functional theory calculations, achieving a 90% accuracy for aromatic molecular switches. The conductive switching behaviors were further confirmed by nonequilibrium Green's function transport calculations.

Rapid Classification of Serum from Patients with Paracoccidioidomycosis Using Infrared Spectroscopy, Univariate Statistics, and Linear Discriminant Analysis (LDA).

Paracoccidioidomycosis (PCM) is a systemic mycosis that is diagnosed by visualizing the fungus in clinical samples or by other methods, like serological techniques. However, all PCM diagnostic methods have limitations. The aim of this study was to develop a diagnostic tool for PCM based on Fourier transform infrared (FTIR) spectroscopy. A total of 224 serum samples were included: 132 from PCM patients and 92 constituting the control group (50 from healthy blood donors and 42 from patients with other systemic mycoses). Samples were analyzed by attenuated total reflection (ATR) and a t-test was performed to find differences in the spectra of the two groups. The wavenumbers that had p < 0.05 had their diagnostic potential evaluated using receiver operating characteristic (ROC) curves. The spectral region with the lowest p value was used for variable selection through principal component analysis (PCA). The selected variables were used in a linear discriminant analysis (LDA). In univariate analysis, the ROC curves with the best performance were obtained in the region 1551-1095 cm-1. The wavenumber that had the highest AUC value was 1264 cm-1, achieving a sensitivity of 97.73%, specificity of 76.01%, and accuracy of 94.22%. The total separation of groups was obtained in the PCA performed with a spectral range of 1551-1095 cm-1. LDA performed with the eight wavenumbers with the greatest weight from the group discrimination in the PCA obtained 100% accuracy. The methodology proposed here is simple, fast, and highly accurate, proving its potential to be applied in the diagnosis of PCM. The proposed method is more accurate than the currently known diagnostic methods, which is particularly relevant for a neglected tropical mycosis such as paracoccidioidomycosis.

Drinking water quality evaluation in supply systems in Wuhan, China: application of entropy weight water quality index and multivariate statistical analysis.

In this study, source water, finished water, and tap water were sampled monthly from two large drinking water treatment plants in Wuhan city, China for 12 months where physicochemical and microbiological parameters were measured, and the complex monitoring data was analyzed using single-factor assessment method, entropy weight water quality index (EWQI), and multivariate statistical techniques (i.e., cluster analysis (CA), discriminant analysis, and correlation analysis). The results of the single-factor assessment method showed that the total nitrogen pollution was the main problem in the source water quality, and the finished and tap water met the required quality standards. The EWQI values indicated that the overall quality of the source, finished, and tap water samples was "Excellent." In addition, strengthening monitoring of parameters with high entropy weights, including Pb, Hg, sulfide, Cr in surface water and Hg, aerobic bateria count, and As in drinking water, were suggested, as they were prone to drastic changes. Spatial CA grouped the finished and tap water samples from the same plant into a cluster. Temporal CA grouped 12 sampling times of source water into Cluster 1 (June), Cluster 2 (April-May, and July-November), and Cluster 3 (December-March). Concerning finished and tap water, except the October was regrouped, the result of temporal CA was consistent to that of the source water. Based on similar characteristics of water samples, monitoring sites and frequency can be optimized. Moreover, stepwise discriminant analysis indicated that the spatiotemporal variations in water quality among CA-groups were enough to be explained by four or five parameters, which provided a basis for the selection of monitoring parameters. The results of correlation analysis showed that few pairwise correlations were both significant (P < 0.05) and stable across sampling sites, suggesting that the number of monitoring parameters was difficult to reduce through substitution. In summary, this study illustrates the usefulness of EWQI and the multivariate statistical techniques in the water quality assessment and monitoring strategy optimization.

ANALYSIS OF FINANCIAL DISTRESS IN VARIOUS INDUSTRIAL SECTOR COMPANIES LISTED ON THE BEI ​​FOR THE 2016-2021 PERIOD

AbstractThis study aims to detect financial distress in various industrial sector companies using the Modified Altman Z-Score Model (1995). Financial distress occurs when the company is in an unhealthy state but has not yet gone bankrupt. The population used is the various industrial sector companies for the 2016-2021 period. The sample in this study were 26 companies with a research period of 6 years so as to produce 156 samples obtained by purposive sampling technique. The data analysis technique used is descriptive statistical analysis and discriminant regression analysis using the SPSS statistical test tool. The results of this study indicate that the variables of WCTA, RETA, EBITA, and BVETL have a simultaneous effect on financial distress. Partially, the variable WCTA has no effect on financial distress. While partially RETA, EBITA, BVETL affect financial distress. Keywords: Altman Z-Score Modification and Financial Distress

Harmonization of ANA testing challenge: quantification strategy to accurately predict end-point titers avoiding serial dilution.

Despite the advantages of automated systems for antinuclear antibody (ANA) analysis, the prediction of end-point titers avoiding serial dilutions is still in progress. The aims of this study were to set a conversion table providing discriminant ranges of fluorescence signal intensity values (FI) corresponding to the end-point titers and validate this tool in a real-life laboratory setting. Eight hundred ninety-four serum samples were analyzed for ANA using Image Navigator System. In order to classify FI into non-overlapping groups corresponding to conventional end-point titers, statistical discriminant analysis was used. Validation study was performed calculating agreement and error rates between visual readings and conversion table of 1119 routine ANA positive samples. Setting of FI ranges corresponding to the end-point titers for different staining patterns was computed. For samples showing single pattern, the overall agreement between visual readings and conversion table was 98.4% for all titers ranging from 1:160 to 1:2560, of which 68.0% had the same titer and 30.4% were within ± one titer difference. Concordance rates according to ANA patterns were as follows: (1) nuclear 98.4%, of which 67.0% had the same titer and 31.4% ± one titer; (2) cytoplasmic 100%, of which 72.7% had the same titer and 27.3% than ± one titer; (3) mitotic 66.6%, of which 33.3% had more ± one titer. Our study developed a quantification method for autoantibodies titers assessment based on just one single sample dilution instead of traditional serial dilution approach, providing significant advantages in routine laboratory in terms of reduction in hand-on time and harmonization of results.

Discriminant Analysis Using Gabor Filter Sets for Lobular Endocervical Glandular Hyperplasia: Numerical Interpretation of Nuclear Atypia by Gabor Filter Features

Introduction: Lobular endocervical glandular hyperplasia (LEGH) is a benign lesion; however, it is considered to be the origin of gastric-type adenocarcinoma in the uterine cervix, and early diagnosis is important. At Shinshu University Hospital, screening of LEGH cells is based on the difference in color tone of cytoplasmic mucin on Papanicolaou staining and detection of gastric mucin using HIK1083-labeled latex agglutination assay. However, it is sometimes difficult to distinguish LEGH cells with subtle nuclear atypia from endocervical (EC) cells. Methods: We calculated the Gabor filter features (mean signal value, standard deviation, skewness, kurtosis) from the nuclei of cytological specimens in EC cells (37 cases) and LEGH cells (33 cases) using microscopic images, and we performed statistical analysis and discriminant analysis by linear support vector machine (LSVM) using these features. A Gabor filter is a linear filter defined as a mathematical representation of the mammalian visual system. Gabor filters with three wavelengths and eight angles were used for analysis. Results: Gabor filter features in EC cells were higher than in LEGH cells, demonstrating that the gradient of LEGH cell nuclei was milder than that of EC cell nuclei. The accuracy calculated using all Gabor filters was 91.0% and the accuracy of four Gabor filters (λ = 2/3π and θ = 0°, 45°, 90°, 135°) was 88.9%. High accuracy with low computation costs was achieved by reducing the number of features used for LSVM. Conclusion: The application of a Gabor filter with convolutional processing resulted in the edges of LEGH cells being slightly rough and thick, whereas those of EC cells were fine and thin. Thus, it is thought that the frequency of abrupt gradients of pixels was higher in EC cells than in LEGH cells, and the gradient of chromatin distribution in LEGH cell nuclei was milder than that in EC cell nuclei. It was possible to evaluate nuclear findings of EC and LEGH cells objectively by quantifying morphological features of nuclei using Gabor filters. It was possible to differentiate EC cells from LEGH cells using LSVM using Gabor filter features.

The visceral adipose tissue bacterial microbiota provides a signature of obesity based on inferred metagenomic functions.

Metabolic inflammation mediated obesity requires bacterial molecules to trigger immune and adipose cells leading to inflammation and adipose depot development. In addition to the well-established gut microbiota dysbiosis, a leaky gut has been identified in patients with obesity and animal models, characterized by the presence of a tissue microbiota in the adipose fat pads. To determine its potential role, we sequenced the bacterial 16 S rRNA genes in the visceral adipose depot of patients with obesity. Taking great care (surgical, biochemical, and bioinformatic) to avoid environmental contaminants. We performed statistical discriminant analyses to identify specific signatures and constructed network of interactions between variables. The data showed that a specific 16SrRNA gene signature was composed of numerous bacterial families discriminating between lean versus patients with obesity and people with severe obesity. The main discriminant families were Burkholderiaceae, Yearsiniaceae, and Xanthomonadaceae, all of which were gram-negative. Interestingly, the Morganellaceae were totally absent from people without obesity while preponderant in all in patients with obesity. To generate hypotheses regarding their potential role, we inferred metabolic pathways from the 16SrRNA gene signatures. We identified several pathways associated with adenosyl-cobalamine previously described to be linked with adipose tissue development. We further identified chorismate biosynthesis, which is involved in aromatic amino-acid metabolism and could play a role in fat pad development. This innovative approach generates novel hypotheses regarding the gut to adipose tissue axis. This innovative approach generates novel hypotheses regarding the gut to adipose tissue axis in obesity and notably the potential role of tissue microbiota.

Phthalate acid esters promoted the enrichment of chlorine dioxide-resistant bacteria and their functions related to human diseases in rural polyvinyl chloride distribution pipes

Polyvinyl chloride (PVC) pipes are widely used as drinking water distribution pipes in rural areas of China. However, whether phthalate acid esters (PAEs) released from PVC pipes will affect tap water quality is still unknown. The influence of released PAEs on the water quality was analysed in this study, especially after ClO2 disinfection. The results indicated that ClO2 disinfection could control the growth of total coliforms and heterotrophic bacteria (HPC). However, when the ClO2 residual decreased to below 0.10 mg/L, HPC and opportunistic pathogens, including Mycobacterium avium and Pseudomonas aeruginosa, increased significantly. In addition, after ClO2 disinfection, PAEs concentrations increased from 10.6–22.2 μg/L to 21.2–58.8 μg/L in different sampling cites. Linear discriminant analysis (LDA) effect size (LEfSe) and statistical analysis of metagenomic profiles (Stamp) showed that ClO2 disinfection induced the enrichment of Pseudomonas, Bradyrhizobium, and Mycobacterium and functions related to human diseases, such as pathogenic Escherichia coli infection, shigellosis, Staphylococcus aureus infection, and Vibrio cholerae infection. The released PAEs not only promoted the growth of these ClO2-resistant bacterial genera but also enhanced their functions related to human diseases. Moreover, these PAEs also induced the enrichment of other bacterial genera, such as Blastomonas, Dechloromonas, and Kocuria, and their functions, such as chronic myeloid leukaemia, African trypanosomiasis, leishmaniasis, hepatitis C and human T-cell leukaemia virus 1 infection. The released PAEs enhanced the microbial risk of the drinking water. These results are meaningful for guaranteeing water quality in rural areas of China.

Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods

The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options for studying plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques that can be used to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants’ signals to gather information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose Zamioculcas zamiifolia and Solanum lycopersicum (tomato) to four different stimuli: wind, heat, red light and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning, for the classification problem of determining the stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy, without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to derive a phytosensing approach to monitoring air pollution in urban areas.

Microbiome in paired root apices and periapical lesions and its association with clinical signs in persistent apical periodontitis using next-generation sequencing.

To assess and compare the microbiome of paired root apices and periapical lesions from cases with failed endodontic treatment and to associate the microbiome and bacterial metabolic pathways in both sites with asymptomatic apical periodontitis (AAP) and symptomatic apical periodontitis (SAP), using next-generation sequencing (NGS). Matched root apices and periapical lesions of patients with failed root canal treatments were surgically extracted. Specimens were cryopulverized, bacterial DNA was extracted and the V3-V4 hypervariable regions of the 16 S rRNA gene were amplified and sequenced using the Illumina Miseq platform. Diversity and community composition were studied in the paired samples, as well as in AAP and SAP cases. Diversity indices were compared in each case by means of the Wilcoxon matched-pairs signed rank and Mann-Whitney U tests. Differences in the community composition were explored with multivariate statistical analysis and Linear discriminant analysis Effect Size (LEfSe). Bacterial functional study was performed through the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis. Twenty-one paired apices and lesions were successfully sequenced and analysed, identifying a total of 21 phyla and 600 genera. A higher alpha-diversity was observed in the periapical lesions, although no global differences in the community composition between the two sites were found (p=.87), the most prevalent genera being Fusobacterium, Porphyromonas and Streptococcus. Prevotella, Clostridiales_vadinBB60_group, Bosea, Phreatobacter, Afipia and Xanthobacteriaceae_unclassified were enriched in SAP samples, while Pseudopropionibacterium, Campylobacter and Peptoniphilus were significantly more abundant in AAP cases (p < .05). Metabolic pathways involved in the amino acid metabolism or degradation and flagellum assembly were more abundant in SAP samples, whereas glucose metabolism-related pathways were associated with AAP. The bacterial community composition was similar in the apices and periapical lesions. The microbiome was different in AAP and SAP samples, gram-negative bacteria showing higher relative abundances in SAP cases. An association was observed between amino acid degradation and flagellum assembly pathways, and the development of tenderness to percussion or palpation.

Thermal comfort conditions in Brazil: A discriminant analysis through the ASHRAE Global Thermal Comfort Database II

The Predicted Mean Vote (PMV) created by Fanger in the 70's is the most common model for estimating the thermal sensation in a group of individuals. The PMV contains some discrepancies related to the thermal reality of the environments where it is applied; thus, several researchers have developed alternative models over the past years in order to reduce these differences. This research aimed at analyzing thermal comfort conditions in Brazil by applying the PMV model and alternative models through a statistical discriminant analysis. By using the ASHRAE Global Thermal Comfort Database II, the environmental and personal variables of thermal comfort were applied in order to calculate the responses of each model and thus compare them to the responses of thermal sensation reported by people in four Brazilian cities: Brasília, Recife, Maceió and Florianópolis. The models used in this research were selected by applying the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) method. A discriminant analysis was performed in order to classify people according to their thermal sensation in different groups, through the development of discriminant functions; this procedure allowed a better visualization of these groups. With the results, the discrimination of individuals was verified according to their thermal sensations, where the models were right to classify individuals into groups with 96.1% for Brasília and Recife; 99.1% for Florianópolis and 99.8% for Maceió, thus showing the effectiveness of the models.

P2P Lending Default Prediction Based on AI and Statistical Models.

Peer-to-peer lending (P2P lending) has proliferated in recent years thanks to Fintech and big data advancements. However, P2P lending platforms are not tightly governed by relevant laws yet, as their development speed has far exceeded that of regulations. Therefore, P2P lending operations are still subject to risks. This paper proposes prediction models to mitigate the risks of default and asymmetric information on P2P lending platforms. Specifically, we designed sophisticated procedures to pre-process mass data extracted from Lending Club in 2018 Q3–2019 Q2. After that, three statistical models, namely, Logistic Regression, Bayesian Classifier, and Linear Discriminant Analysis (LDA), and five AI models, namely, Decision Tree, Random Forest, LightGBM, Artificial Neural Network (ANN), and Convolutional Neural Network (CNN), were utilized for data analysis. The loan statuses of Lending Club’s customers were rationally classified. To evaluate the models, we adopted the confusion matrix series of metrics, AUC-ROC curve, Kolmogorov–Smirnov chart (KS), and Student’s t-test. Empirical studies show that LightGBM produces the best performance and is 2.91% more accurate than the other models, resulting in a revenue improvement of nearly USD 24 million for Lending Club. Student’s t-test proves that the differences between models are statistically significant.

Perceived quality factors that discriminate parents of orthodontic patients according to their satisfaction

PurposeMonitoring the perception of quality by parents and the satisfaction with their children’s treatment is crucial in developing high-level health-care services. The purpose of this paper is to identify the perceived quality factors by parents of paediatric orthodontic patients according to the levels of satisfaction.Design/methodology/approachA survey with 316 parents of paediatric orthodontic patients of 45 offices in a big Brazilian city was conducted. The questionnaires included statements on the parents’ satisfaction and perception of quality concerning their children’s treatment. The data were analysed using multivariate statistics (exploratory factor analysis, cluster analysis and discriminant analysis).FindingsParents were classified into three clusters according to their level of satisfaction. This survey identified 11 quality factors perceived by parents of patients regarding their children’s orthodontic treatment. Among them, five factors discriminated the clusters: orthodontist’s technical skills, administrative organisation, location (accessibility and convenience), treatment duration and dental supplies.Practical implicationsThis research identified the perceived quality factors that most impacted parents of orthodontic patients’ satisfaction, contributing to dentists in developing strategies and actions to improve the quality of services in dental offices.Originality/valueThis paper included 11 perceived quality factors in the hypothetical model, identified through literature review and a qualitative phase. Moreover, multivariate statistical analyses confirmed the validity and reliability of the questionnaires, classified respondents in clusters and identified the perceived quality factors that most discriminated them. Therefore, the current investigation presented a more comprehensive and robust analysis than the previous studies on this topic.

Chemical and granulometric characterization of soil and its influence on the bromatologic composition of pastures in savannah region, Central Brazil

Livestock is responsible for much of the world's land use, and grazing areas are the main livelihood of the world's largest commercial herd. Most of the pasture areas have low productivity because they are at some level of degradation. This study aimed to apply multivariate analysis and remote sensing techniques for to evaluate the influence of soil granulometric chemical characteristics and bromatologic composition associated with vegetative index classes of pastures cultivated in the Savana biome, region of Central Brazil. The pasture areas were selected according to Normalized Difference Vegetation Index (NDVI) considering the annual average and their classification into a low vegetative index (LVI), medium vegetative index (MVI), and high vegetative index (HVI) levels. Forage material collections were carried out on six dates (bimonthly) and a soil collection date. Productive parameters of the pasture were evaluated: fresh mass (FM), dry mass (DM) and tillers (TILL); pasture bromatologicals: crude protein (CP), total dry matter (TDM), dry matter, ether extract (EE), neutral detergent fiber (NDF) and acid detergent fiber (ADF), and non-woven fabric (NW); the chemical composition of the soil: pH, Ca, Mg, Al, total acidity, P, K, cation exchange capacity (CEC), organic matter (OM), base saturation; and soil granulometric: clay, silt, and sand contents. The results were submitted to descriptive statistical analysis and discriminant analysis by partial least squares (PLS-DA). The PLS-DA analysis method allowed us to differentiate pasture areas in terms of vegetative indices (HVI and LVI) through the chemical and granulometric parameters of the soil and by the productive and bromatological parameters of the pastures. The granulometric characteristics of the soils were the parameters with the lowest weight to differentiate the pastures in terms of low, medium, and high vegetative indexes. Pastures with high vegetative index (HVI), even in intensive systems, showed better chemical quality (Mg, P, K, OM, CEC, and Ca) of soils and bromatological quality of forages (FM, ADF, PB, and TDM).

Development of a non-destructive methodology using ATR-FTIR and chemometrics to discriminate wild silk species in heritage collections

This paper aims to develop a non-destructive methodology applicable to heritage artifacts in order to discriminate between different species of wild silks. Wild silks are less known than domestic silk from Bombyx mori, but they are numerous and have been used in textile weaving for thousands of years. Archaeological artifacts, museum artifacts, and ethnographic collections deserve to be better documented regarding wild silks. The developed methodology is based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR) coupled with chemometric analyses such as Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Discriminant statistical analysis has enabled within a corpus of wild silks, including cocoons from the collections of the musée du quai Branly-Jacques Chirac (Paris, France), to differentiate cocoons of the species Borocera madagascariensis (Lasiocampidae) from samples belonging to the Saturniidae family. These very encouraging results are promising for future studies involving more species and more diverse artifacts.

Phenotypic Characterization of Chronic Kidney Patients Through Hierarchical Clustering.

Chronic kidney disease is a major public health problem around the world and this disease early diagnosis is still a great challenge as it is asymptomatic in its early stages. Thus, in order to identify variables capable of assisting CKD diagnosis and monitoring, machine learning techniques and statistical analysis use has shown itself to be extremely promising. For this work, unsupervised machine learning, statistical analysis techniques and discriminant analysis were used.Clinical Relevance - Discriminating variables characterization assist to differentiate groups of patients in different stages of Chronic Kidney Disease and it has important outcomes in the development of future models to aid clinical decision-making, as they can generate models with a greater predictive capacity for Chronic Kidney Disease, predominantly aiding the early diagnosis capacity of this pathology.

Dynamic Analysis of Tourism Competitiveness of the European Countries Based on Discriminant Statistical Analysis

Dynamic Analysis of Tourism Competitiveness of the European Countries Based on Discriminant Statistical Analysis

PLS-DA and data fusion of visible Reflectance, XRF and FTIR spectroscopy in the classification of mixed historical pigments

In this work samples of historical pigments of green hue were brushed on a canvas and studied by Visible Reflectance, X-Ray Fluorescence and Fourier Transform Infrared Spectroscopy. One hundred samples were investigated, all with green hue, these prepared from pigments themselves green, such as chromium oxide (Cr2O3) or from a mixture of pigments that result in green, for example, chrome yellow (PbCrO4) and Prussian blue (Fe4[Fe(CN)6]3). Because every sample investigated through the spectroscopic techniques were of green hue, the characterization of the pigments present in the mixtures through the visual inspection of spectra has become a complex task in some cases, also, due the large number of recorded spectra. In this work, classification models were developed using the multivariate statistical method Partial Least Squares Discriminant Analysis (PLS-DA) to automate the characterization of the pigments present in the mixtures. The models were developed to classify chromium oxide (Cr2O3), chrome yellow (PbCrO4), cerulean blue (CoO.nSnO2) and yellow ochre (Fe2O3·H2O + clay + silica). The models were developed from the fusion of data from the three spectroscopic techniques. However, before data fusion, pre-treatments of the spectral data were tested for their influence on the PLS-DA models. The models developed with data from the three techniques made it possible to classify the pigments of interest in the samples with up to 100% effectiveness. The results also indicate that fusion of the data from the three techniques allows to obtain fingerprints of the pigments of interest, which is not always possible using data from only one or two of the techniques applied in this work.

Psychological Resources of the Modification in Self-Fulfillment of Personality

The aim of the article is an empirical definition of the psychological resources of modification in the self-fulfillment of the person. The empirical study used methods of psychological questioning, as well as such methods of mathematical and statistical data analysis, in particular, regression, correlation, classification, discriminant, and multifactor analysis. The empirical study was implemented according to Nelson’s model, which made it possible to characterize the required psychological resources by discriminating against them. Research results. It was found that the reflected psychological resources in the configurations of self-fulfillment – existential, personal, and professional self-fulfillment – are: creativity, work on yourself, the ability to update their own resources, coherence, charity, meaningful life, expression of emotions. Interpretive resources «creativity» and «ability to update own resources» are predictors of all three configurations of self-realization. Psychological resources that moderate the relationship of existential motivation as a manifestation of the inner world of man and life tasks are identified as creativity, self-work, coherence, charity, expression of emotions. Psychological resources that are factors of self-realization, self-actualization, self-fulfillment of the person are creativity, the ability to update their own resources, coherence. Coherence is a factor for all three configurations of self-realization. Conclusions. The sequence of the resourcefulness of changes in self-fulfillment is as follows: first, due to the resource of work on oneself, self-actualization is carried out, the effect of which is existential self-fulfillment as a sense of real and authorial life lived by the person; further, the ability to update their own resources leads to the transformation of forms in personal and professional self-fulfillment; at the end of the change, the resource of creativity affects the possibility of self-fulfillment of the person. The whole process of reflected changes is patronized by coherence, as a factor in the significance of a life lived by the person, as well as its optimal and healthy functioning.