Multivariate classification of cotton cultivars tolerant to salt stress

ABSTRACT Two multivariate methods were adopted to classify salt-tolerant cotton genotypes based on their growth and physiological traits. The genotypes were cultivated in a greenhouse and subjected to 45 days of irrigation with saline water from the V4 phase onwards. Irrigation was performed with saline water with electrical conductivity (ECw) of 6.0 dS m-1. A factorial-randomized block design was adopted with nine cultivars, two treatments of ECw (0.6 as the control, and 6.0 dS m-1), and four replicates. Plants were evaluated for growth, gas exchange, and photosynthesis. The data were statistically analyzed using univariate and multivariate methods. For the latter, non-hierarchical (principal component, PC) and hierarchical (UPGMA) models were used for the classification of cultivars. Significant differences were found between cultivars based on univariate analyses, and the traits that differed statistically were used for multivariate analyses. Four groups were identified with the same composition in both the PC and UPGMA methods. Among them, one contained the cultivars BRS Seridó, BRS 286, FMT 705, and BRS Rubi, which were tolerant to salt stress imposed on the plants. Photosynthesis, transpiration, and stomatal conductance data were the main contributors to the classification of cultivars using the principal component method.

Child Swaddling or Back Wrapping During Street Trading

The study is premised on social responsibility and social epidemiological theories and examined the exposure of back-wrapped babies to health risk during street trading. Data were collected using structured face-to-face interviews and snowballing techniques among 228 Street trading women (with children aged ≤ 11 months), in one local government area of Ogun State, Nigeria. Data analyses involved univariate and multivariate methods. The results show that 58.3% of women interviewed wrapped their babies at their back while trading on the streets, ≥80% were not aware of any campaign against baby back-wrapping, 35% viewed baby back-wrapping as medicinal for the baby, and as traditional practice (59.2%). The multivariate analysis revealed that children wrapped while trading on the street are at higher risk of exposure to illness than those not back wrapped (OR=1.778, p=0.042). The authors suggested media campaign against back-wrapping baby while trading on the street to reduce exposure to diseases, mortalities and possibly achievement of sustainable development goal (SDG-3).

The Use of Multivariate Data Analysis (HCA and PCA) to Characterize Ashes from Biomass Combustion

The content of heavy metals Cd, Cr, Cu, Fe, Ni, Pb and Zn in ash samples from miscanthus, oak, pine, sunflower husk, wheat straw, and willow ashes burned at 500, 600, 700, 800, 900, and 1000 °C, respectively, was determined. The statistical analysis of the results was based on multivariate methods: hierarchical cluster analysis (HCA), and principal component analysis (PCA), which made it possible to classify the raw materials ashed at different temperatures into the most similar groups, and to study the structure of data variability. Using PCA, three principal components were extracted, which explain more than 88% of the variability of the studied elements. Therefore, it can be concluded that the application of multivariate statistical techniques to the analysis of the results of the study of heavy metal content allowed us to draw conclusions about the influence of biomass properties on its chemical characteristics during combustion.

Hierarchy of homeostasis mechanisms of human-machine complexes: problems of their analysis and Implementation

Developing control systems for regional socio-economic and large technical systems is inevitably associated with the concept of human-machine complexes (HMC). They are considered as a set of a large number of hierarchically dependent complex subsystems, including staffs and machines, possessing a certain degree of organization and autonomy, interconnected by mechanisms and means of organization (i.e. material and informational links) to ensure the purposeful functioning of the entire system as a single whole in conditions of tense internal resource close to the limiting ones. The article discusses the hierarchy of interrelated homeostasis mechanisms of the HMC, ensuring both its parameter constancy and the performance of systemic functions at all hierarchy levels. In particular, the following types of homeostasis are considered: a parametric type (the internal circuit of homeostasis), designed to maintain the parameter constancy of HMC active elements and a functional type (the external circuit of homeostasis), ensuring the constancy of its functioning. At the same time, the functional integrity of the system is ensured by the work of the interrelated static-dynamic and entropy-organizational homeostasis mechanisms, which, in turn, in practical activity are implemented through coordination-motivational (CMR), organizational-motivational (OMP) and functional (FMR) mechanisms of regulation. The need for an integrated application of all entropy-organizational regulation mechanisms (CMR, OMR, FMR) in the operators’ activities determines the necessity to use multivariate methods to determine their composition and application. To solve this problem, the article examines the supersystem elements of the activity regulation, which are formed as a result of the operators’ psychological interaction in the process of their activity, as a kind of an abstract system of a higher order, which has its own supersystem properties, its own autonomous metric and conservation laws, and most importantly, its situation reflection which is different from the system one. In this case, homeostatic hierarchical networks, the elements of which are homeostatic mechanisms of HMC operators at various levels, become the basis of HMC structural-hierarchical homeostasis. Thus, being complex systems, HMC synergistically change (adapt) their internal characteristics, thereby ensure the integrity of the entire system functioning, which allows speaking, on the one hand, of their homeostaticity as the HMC most important characteristic, and on the other hand, determining the need to search for new approaches to their methodological description, and, consequently, to organizing their management and design.

Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods

The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction (RP2) was 0.9504 and the root mean square error of prediction (RMSEP) was 0.8181. For the stearic acid, RP2 and RMSEP were 0.9636 and 0.2965. In the prediction of arachidic acid, RP2 and RMSEP were 0.9576 and 0.0577. In the prediction of behenic acid, the RP2 and RMSEP were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils.

A comparison of children’s diet and movement behaviour patterns derived from three unsupervised multivariate methods

Background Behavioural patterns are typically derived using unsupervised multivariate methods such as principal component analysis (PCA), latent profile analysis (LPA) and cluster analysis (CA). Comparability and congruence between the patterns derived from these methods has not been previously investigated, thus it’s unclear whether patterns from studies using different methods are directly comparable. This study aimed to compare behavioural patterns derived across diet, physical activity, sedentary behaviour and sleep domains, using PCA, LPA and CA in a single dataset. Methods Parent-report and accelerometry data from the second wave (2011/12; child age 6-8y, n = 432) of the HAPPY cohort study (Melbourne, Australia) were used to derive behavioural patterns using PCA, LPA and CA. Standardized variables assessing diet (intake of fruit, vegetable, sweet, and savoury discretionary items), physical activity (moderate- to vigorous-intensity physical activity [MVPA] from accelerometry, organised sport duration and outdoor playtime from parent report), sedentary behaviour (sedentary time from accelerometry, screen time, videogames and quiet playtime from parent report) and sleep (daily sleep duration) were included in the analyses. For each method, commonly used criteria for pattern retention were applied. Results PCA produced four patterns whereas LPA and CA each generated three patterns. Despite the number and characterisation of the behavioural patterns derived being non-identical, each method identified a healthy, unhealthy and a mixed pattern. Three common underlying themes emerged across the methods for each type of pattern: (i) High fruit and vegetable intake and high outdoor play (“healthy”); (ii) poor diet (either low fruit and vegetable intake or high discretionary food intake) and high sedentary behaviour (“unhealthy”); and (iii) high MVPA, poor diet (as defined above) and low sedentary time (“mixed”). Conclusion Within this sample, despite differences in the number of patterns derived by each method, a good degree of concordance across pattern characteristics was seen between the methods. Differences between patterns could be attributable to the underpinning statistical technique of each method. Therefore, acknowledging the differences between the methods and ensuring thorough documentation of the pattern derivation analyses is essential to inform comparison of patterns derived through a range of approaches across studies.