Mutual Correlations Research Articles

Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

Modelling plays an important role in identifying and solving problems that arise in a number of scientific issues including agriculture. Research in the natural environment is often costly, labour demanding, and, in some cases, impossible to carry out. Hence, there is a need to create and use specific “substitutes” for originals, known in a broad sense as models. Owing to the dynamic development of computer techniques, simulation models, in the form of information technology (IT) systems that support cognitive processes (of various types), are acquiring significant importance. Models primarily serve to provide a better understanding of studied empirical systems, and for efficient design of new systems as well as their rapid (and also inexpensive) improvement. Empirical mathematical models that are based on artificial neural networks and mathematical statistical methods have many similarities. In practice, scientific methodologies all use different terminology, which is mainly due to historical factors. Unfortunately, this distorts an overview of their mutual correlations, and therefore, fundamentally hinders an adequate comparative analysis of the methods. Using neural modelling terminology, statisticians are primarily concerned with the process of generalisation that involves analysing previously acquired noisy empirical data. Indeed, the objects of analyses, whether statistical or neural, are generally the results of experiments that, by their nature, are subject to various types of errors, including measurement errors. In this overview, we identify and highlight areas of correlation and interfacing between several selected neural network models and relevant, commonly used statistical methods that are frequently applied in agriculture. Examples are provided on the assessment of the quality of plant and animal production, pest risks, and the quality of agricultural environments.

Influence of surface chemical properties of nanocrystalline CeO2 on phosphate adsorption and methyl-paraoxon decomposition

Nanocrystalline cerium oxide (CeO2) was used as an effective catalytic and adsorption material for the safe removal of selected phosphorus compounds. Several CeO2 samples were prepared by a simple coprecipitation method with subsequent annealing at temperatures between 400–600 °C, and their sorption ability for inorganic phosphates was studied together with their ability to decompose the organophosphorus compound - paraoxon methyl (PO) - with the goal to find the relations between the sorption and catalytic activity of the prepared materials. It was found that a complex set of physical and chemical characteristics (surface area, crystallite size, pore volume/size, the number of surface hydroxyl groups) plays a crucial role in the governing of adsorption ability and catalytic activity. From the dependence of some characteristics (i.e., the number of surface hydroxyl groups, BET surface area, crystallite size) on the annealing temperature and their mutual correlations, the model describing the degradation of organophosphate compounds on the surface of cerium oxide was deduced.

The Impact of COVID-19 on Purchase Behavior Changes in Smart Regions

The COVID-19 pandemic has changed consumer behavior due to various restrictions and increased degrees of ICT use. By establishing and verifying the validity of the hypotheses, we aim to compare intensities of mutual correlations that indicate changes in consumer behavior depending on the degree and nature of changes in selected socio-demographic or socio-economic factors. The statistical evaluation of the answers obtained in surveys of representative samples of 987 respondents from the Slovak Republic (implemented in 2021 about the dual quality of goods sold in the EU) and also the answers of 347 respondents (in 2022 aimed at changes in Slovak consumer behavior) will be carried out with multivariate analyses using the SPSS program. The outputs indicated that during self-isolation periods, Slovak consumers bought more or the same amount as before the pandemic; shopping habits were mainly changed by women and groups with lower household income. Test subjects preferred the quality products and products posing the least amount of risk to health. All consumers intend to continue to shop through e-commerce platforms where they prefer a more personal experience (through social media or YouTube). Low-income people’s budgets are threatened by cheap products and poor distribution of spending, especially among young people. We recommend simplifying personalized visualized sales and education content and e-methods of information sharing also in order to make them accessible to digitally disadvantaged groups (according to income, age, education, etc.). The use of blockchains increases transparency of production and sales value chains, reducing the occurrence of unfair practices, and promoting participatory public dialogue.

Superradiant Electron Energy Loss Spectroscopy.

We analyze the interaction between a free electron and an ensemble of identical optical emitters. The mutual coherence and correlations between the emitters can enhance the interaction with each electron and become imprinted on its energy spectrum. We present schemes by which such collective interactions can be realized. As a possible application, we investigate free-electron interactions with superradiant systems, showing how electrons can probe the ultrafast population dynamics of superradiance.

The global historical climate database HCLIM

There is a growing need for past weather and climate data to support science and decision-making. This paper describes the compilation and construction of a global multivariable (air temperature, pressure, precipitation sum, number of precipitation days) monthly instrumental climate database that encompasses a substantial body of the known early instrumental time series. The dataset contains series compiled from existing databases that start before 1890 (though continuing to the present) as well as a large amount of newly rescued data. All series underwent a quality control procedure and subdaily series were processed to monthly mean values. An inventory was compiled, and the collection was deduplicated based on coordinates and mutual correlations. The data are provided in a common format accompanied by the inventory. The collection totals 12452 meteorological records in 118 countries. The data can be used for climate reconstructions and analyses. It is the most comprehensive global monthly climate dataset for the preindustrial period so far.

Fatigue, Depression, and Anxiety in Patients with COPD, Asthma and Asthma-COPD Overlap.

Asthma and COPD are extremely common respiratory diseases that have a serious impact on people's lives around the world. A disease characterized by symptoms characteristic for asthma and COPD is called asthma-COPD overlap (ACO). Fatigue and certain psychological disorders such as anxiety and depression are important comorbidities in these diseases. The purpose of this study was to assess the prevalence of fatigue, anxiety, and depression in patients with asthma, COPD, and ACO and to also consider their mutual correlations. A total of 325 patients were enrolled in the study. There were 159 women and 166 men and their mean age was 63. Two standardized questionnaires were used: the Modified Fatigue Impact Scale (MFIS) and the Hospital Anxiety and Depression Scale (HADS). The mean total MFIS score for all patients was 33.03. Patients with asthma generally scored lower than patients with COPD and ACO. There were no statistical differences in the HADS for anxiety between the groups, although around half of the patients registered a score indicating some level of disorder. Patients with COPD and ACO were proven to suffer more from depression than patients with asthma. The HADS and MFIS scores were found to correlate significantly and positively. Our study showed that patients with COPD, asthma, and ACO generally suffered from an increased level of fatigue and depression. Anxiety was high in all groups, but it was at a similar level for patients suffering from each of the three diseases under consideration. It is important to treat the physical symptoms as well as the psychological disorders since they greatly impact on the patient outcomes.

A Machine Learning Method for Engineering Risk Identification of Goaf

The risk evaluation indexes of goaf are multi-source and have complex mutual internal correlations, and there are great differences in the risk identification of goaf from different mines among the various influencing factors. This paper mainly focuses on principal component analysis (PCA) and the differential evolution algorithm (DE), while a multi-classification support vector machine (SVM) is adopted to classify the risks of goaf. Then, the K-fold cross-validation method is used to prevent the overfitting of selection in the model. After the analysis, nine factors affecting the risk identification of goaf in a certain area of East China were determined as the primary influencing factors, and 120 measured goafs were taken as examples for classifying the risks. More specifically, the classification results show that: (1) SVM has the useful ability of generalization, especially when solving the problems of overfitting, and it is easy to fall into the local minima under the conditions of small samples; (2) PCA is employed to realize the intelligent dimensionality reduction and denoising of multi-source impact indicators for goaf risk identification, which immensely improves the prediction accuracy and classification efficiency of the model; (3) after using the DE, the optimal solutions of the problems to be optimized are automatically obtained through the global optimization search mechanism, namely, the kernel function parameter, ‘γ’, and the penalty factor, ‘C’, of the SVM, which further verifies that the characteristics of clear logic, strong convergence, and good robustness can be found in the DE. As demonstrated, this method has the advantages of guiding significance and application value for goaf risk identification.

Transition to Limit-Cycle Oscillation in Fluid-Structure Interactions: Mutual Correlations and Causal Dependencies

We investigate the dynamic characteristics corresponding to the structural fluctuations of a cantilever suspended in a turbulent flow. To investigate the intricate dynamics of the flow–structure interaction, first, we explore the ability of network analysis to identify the different dynamic states and probe the viability of using quantifiers of network topology as precursors for the onset of limit-cycle oscillations. By increasing the Reynolds number, we observe that the structural oscillations, measured using a strain gauge, transition from low-amplitude chaotic oscillations to large-amplitude periodic oscillations associated with limit-cycle oscillations. We characterize the dynamic states of the system by constructing the weighted correlation network from the time series of strain and identifying the network properties that have the potential to be used as precursors for the onset of limit-cycle oscillations. Furthermore, we use Pearson correlation to illustrate the evolution of mutual statistical influence between the structural oscillations and the flowfield. We use this information and the Granger causality to identify the causal dependence between the structural oscillations and velocity fluctuations. By identifying the causal variable during each regime, we illustrate the directional dependence through a cause–effect relationship in this flow–structure interaction as it transitions to limit-cycle oscillations.

Awareness of age-related changes in Norwegian individuals 50+. Short form questionnaire validation.

A questionnaire assessing awareness of positive and negative age-related changes (AARC gains and losses) was developed in the US and Germany, and validated for the UK and Brazilian populations. In this study, we validated the short-form measure (AARC-10 SF) in the Norwegian population aged 50 and over. In addition, the relationship between cognitive variables and AARC was examined. Cross-sectional analyses of data from 1,510 participants in the ongoing online PROTECT Norge study were used to explore and confirm the two-factor structure of AARC gains and AARC losses; reliability; measurement invariance across different population groups defined by sex, education level, employment, and in middle age, early old age, and advanced old age. We explored the relationship between AARC and demographic variables (defined in the same way as the population groups). We confirmed the two-factor structure (gains and losses) of the Norwegian translation of the AARC-10 SF. We did not find mutual correlations between related items in gains and losses, except for the physical health item from the gains dimension, which was positively correlated with all items of the losses dimension. Age, sex, marital status, employment, and university education predicted AARC gains and losses. The Norwegian translation of the AARC-10 SF captures individuals' positive and negative self-perceptions of age-related changes in their mental, physical, and cognitive health.

Анализ метеорологических и солнечных рядов спутниковых наблюдений в пункте Кара-Даг

In this paper, we discuss the results of the analysis of time series of measurements from outer space of local temperatures of the surface of the earth and air at a height of 2 meters, as well as the general insolation of the earth falling on the surface at Kara-Dag over the past 38 years. Mutual correlations were established between the analyzed sequences of local measurements and changes in the length of the day, the number of sunspots, and the indices of changes in global temperature by calculating the functions of the mutual (two-channel) power spectral density.

Application of the Clustering Technique to Multiple Nutritional Factors Related to Inflammation and Disease Progression in Patients with Inflammatory Bowel Disease.

Diet and nutritional status affect intestinal inflammation in patients with inflammatory bowel disease (IBD). The aim of this study was to use a cluster analysis to assess structural similarity between different groups of parameters including short-chain fatty acid (SCFA) levels in stool as well as hematological and inflammatory parameters (such as serum C-reactive protein (CRP) and proinflammatory and anti-inflammatory cytokines). We also assessed similarity between IBD patients in terms of various biochemical features of disease activity and nutritional status. A total of 48 participants were enrolled, including 36 patients with IBD and 12 controls. We identified four main meaningful clusters of parameters. The first cluster included all SCFAs with strong mutual correlations. The second cluster contained red blood cell parameters and albumin levels. The third cluster included proinflammatory parameters such as tumor necrosis factor-α, CRP, platelets, and phosphoric, succinic, and lactic acids. The final cluster revealed an association between zonulin and interleukins IL-10, IL-17, and IL-22. Moreover, we observed an inverse correlation between IL-6 and body mass index. Our findings suggest a link between nutritional status, diet, and inflammatory parameters in patients with IBD, which contribute to a better adjustment of the nutritional treatment.

Hierarchical Dynamic Bayesian Network-Based Fatigue Crack Propagation Modeling Considering Initial Defects.

Orthotropic steel decks (OSDs) are inevitably subjected to fatigue damage caused by cycled vehicle loads in long-span bridges. This study establishes a probabilistic analysis framework integrating the dynamic Bayesian network (DBN) and fracture mechanics to model the fatigue crack propagation considering mutual correlations among multiple fatigue details. Both the observations of fatigue crack length from field inspection and monitoring data of vehicle loads from the weight-in-motion (WIM) system are utilized. First, fracture mechanics-based uncertainty analysis is performed to determine the multiple uncertainty sources in the Paris crack propagation model, material property, and observation data of crack length. The uncertainty of monitoring data of vehicle loads is ignored because of its high accuracy; consequently, the vehicle-load-related uncertainty is spontaneously ignored, which is also demonstrated to be very small on the investigated actual bridges. Second, a hierarchical DBN model is introduced to construct mutual dependencies among various uncertainties and intra-correlations in the propagation process of multiple fatigue cracks at different components. Third, a stochastic traffic model is established based on the WIM monitoring data and multi-scale finite element analysis via substructure techniques to determine the probability distribution of vehicle-load-related parameters. After variable discretization, a refined exact inference algorithm in a forward–backward–forward manner is adopted to estimate the posterior distribution of equivalent initial crack length and update the established DBN model. Finally, the proposed method is demonstrated by a numerical case study and a typical application on an actual cable-stayed bridge with steel box girders using OSDs in China. The results show that the probability distribution of equivalent initial crack size can be spontaneously derived with a larger mean value than the results of conventional empirical analysis. Furthermore, the component-level fatigue reliability is tracked and predicted based on the established crack propagation model.

Indomethacin: The Interplay between Structural Relaxation, Viscous Flow and Crystal Growth.

Non-isothermal differential scanning calorimetry (DSC) was used to study the influences of particle size (daver) and heating rate (q+) on the structural relaxation, crystal growth and decomposition kinetics of amorphous indomethacin. The structural relaxation and decomposition processes exhibited daver-independent kinetics, with the q+ dependences based on the apparent activation energies of 342 and 106 kJ·mol−1, respectively. The DSC-measured crystal growth kinetics played a dominant role in the nucleation throughout the total macroscopic amorphous-to-crystalline transformation: the change from the zero-order to the autocatalytic mechanism with increasing q+, the significant alteration of kinetics, with the storage below the glass transition temperature, and the accelerated crystallization due to mechanically induced defects. Whereas slow q+ led to the formation of the thermodynamically stable γ polymorph, fast q+ produced a significant amount of the metastable α polymorph. Mutual correlations between the macroscopic and microscopic crystal growth processes, and between the viscous flow and structural relaxation motions, were discussed based on the values of the corresponding activation energies. Notably, this approach helped us to distinguish between particular crystal growth modes in the case of the powdered indomethacin materials. Ediger’s decoupling parameter was used to quantify the relationship between the viscosity and crystal growth. The link between the cooperativity of structural domains, parameters of the Tool-Narayanaswamy-Moynihan relaxation model and microscopic crystal growth was proposed.

The Spectral Energy Distributions for 4FGL Blazars

In this paper, the multiwavelength data from radio to X-ray bands for 2709 blazars in the 4FGL-DR3 catalog are compiled to calculate their spectral energy distributions using a parabolic equation . Some important parameters including spectral curvature (P 1), synchrotron peak frequency (P 2, ), and peak luminosity () are obtained. Based on those parameters, we discussed the classification of blazars using the “Bayesian classification” and investigated some mutual correlations. We came to the following results. (1) Based on the Bayesian classification of synchrotron peak frequencies, the 2709 blazars can be classified into three subclasses, i.e., for low synchrotron peak blazars (LSPs), for intermediate synchrotron peak blazars (ISPs), and for high synchrotron peak blazars (HSPs), and there are 820 HSPs, 750 ISPs, and 1139 LSPs. (2) The γ-ray emission has the closest relationship with radio emission, followed by optical emission, while the weakest relationship is that with X-ray emission. The γ-ray luminosity is also correlated with the synchrotron peak luminosity. (3) There are strong positive correlations between the curvature (1/∣P 1∣) and the peak frequency () for all subclasses (FSRQs, (high, intermediate, and low) BL Lacertae objects). For different subclasses, the correlation slopes are different, which implies that there are different acceleration mechanisms and emission processes for different subclasses of blazars.

Assessment of Variability for Nutritional Traits of Burr Medic (Medicago polymorpha L.) Genotypes with Different Phenology

The objective of this study was to determine compatible genotypes for both grazing and ley farming systems concerning nutritional traits among burr medic (Medicago polymorpha L.) genotypes with different flowering times. Therefore, the variability for nutritional traits of early- (n=13), medium- (n=12) and late-flowering (n=19) genotypes from a breeding study carried out during the 2016-2018 years was assessed using one-way ANOVA and chemometric techniques such as principal component (PCA) and cluster (CA) analyses. Except for the acid detergent protein, calcium and magnesium contents, there were significant differences in the nutritional traits among the genotypes with different flowering times. The medium-flowering genotype had a significant advantage over especially early-flowering genotype in crude protein, acid detergent fiber, metabolizable energy, and relative feed value. There were significantly mutual correlations between most of the studied traits. Consequently, considerable amounts of variability were determined among the genotypes for all the traits under consideration. The 44 genotypes formed three clusters, in which cluster sizes ranged from 3 to 29 accessions per cluster. The PCA 1 and 2 had the highest eigenvalues of 6.44 and 1.35, describing 63.27% and 15.91% of the total variance, respectively. The PCA and CA results indicate that medium- and late-flowering genotypes had the best nutritional traits due to probably high photosynthetic capacity in the conditions of the present study.

Comparison of advanced semi‐probabilistic methods for design and assessment of concrete structures

AbstractThis paper presents the comparison of advanced semi‐probabilistic methods for the design and assessment of concrete structures represented by mathematical models solved by non‐linear finite element methods. The special attention is given to the advanced methods focused on the estimation of the coefficient of variation of structural resistance. Numerical examples represent a replication of laboratory experiments of beams with different failure modes. The obtained results are discussed with respect to the accuracy of the employed methods and the influence of the assumed statistical correlation among basic variables. Simplified methods give a good estimation of the design values, though their accuracy is dependent on the type of the failure mechanism. Moreover, it is shown that mutual correlations among random variables may significantly affect the design value of resistance, and they should be carefully defined and modeled.

The status of implementation of photovoltaic systems and its influencing factors in European countries

AbstractThis paper gives an overview of the status of implementation of photovoltaic (PV) systems among 34 member countries involved in the COST Action PEARL PV in the year 2018. Besides this, influencing factors that have an impact on the implementation of PV systems in each country have been assessed. The implementation of PV systems varies in European countries due to differences in solar irradiation and weather conditions, electricity consumption, national economic situation, and governmental approaches to promote PV. For this paper, data sets covering, among others, numbers installed PV systems, and the variables mentioned above, were collected, processed, and synchronized. Subsequently, relations between data were analyzed by geographic mapping and mutual correlations. The 34 countries evaluated produced together 120 TWh of PV electricity in 2018. It was notable that Germany produced the highest amount (45 TWh) and has the highest share (8.4%) of PV in its electricity generation. Weak correlations were found between the different variables and the gross domestic product, annual global irradiation, number of subsidy schemes, cumulative installed capacity, annual electricity generated by PV, the share of RE, and share of PV. Logically, as expected, strong correlations (R2 = 0.9) were found between the number of inhabitants and the annual electricity production and consumption. From our analysis, we conclude that four different groups of countries can be identified to categorize the usability of PV. One type covers countries with a significant unused PV potential while local–global irradiation is redundantly available, such as Turkey, Spain, Cyprus, Portugal, and Israel.

Heralded single-photon source based on an ensemble of Raman-active molecules

Light with high mutual correlations at different frequencies can be used to create heralded single-photon sources, which may serve as the basic elements of existing quantum cryptography and quantum teleportation schemes. One of the important examples in natural systems of light with high mutual correlations is the light produced by spontaneous Raman scattering on an ensemble of molecules. In this paper, we investigate the possibility of using Raman light to create a heralded single-photon source. We show that when using Stokes scattered light for postselection of anti-Stokes scattered light, the latter may possess single-photon properties. We analyze the influence of various negative factors on the characteristics of such a heralded single-photon source, which include a time delay between Stokes and anti-Stokes photons, the finiteness of the correlation radius of an external source, and background radiation. We show that the low value of the second-order autocorrelation function of the single-photon source is preserved even when the flow of uncorrelated photons exceeds the flow of correlated photons in scattered Raman light by an order of magnitude.

Gastrodia elata and parishin ameliorate aging induced ‘leaky gut’ in mice: Correlation with gut microbiota

BackgroundThe aging-induced decrease in intestinal barrier function contributes to many age-related diseases. Studies on preventive measures for “leaky gut” may help improve the quality of life of geriatric patients. The potent anti-aging effect of Gastrodia elata and parishin, which is one of its active ingredients, has been reported previously. However, their effects on the gut remain elusive, and the effect of parishin on mammals has not been studied. MethodsWe used quantitative RT-PCR, western blotting, immunohistochemical analysis, and 16S rRNA sequencing to investigate the effect of G. elata and parishin on the intestinal barrier function of D-Gal-induced aging mice. ResultsG. elata and parishin prevented the decrease in tight junction protein (TJP) expression and morphological changes, modulated the composition of fecal microbiota to a healthier state, and reversed the translocation of microbial toxins and systemic inflammation. The correlation analyses showed that TJP expression and systemic inflammation were significantly positively or negatively correlated with the composition of fecal microbiota after G. elata and parishin administration. Additionally, TJP expression was also correlated with systemic inflammation. Moreover, G. elata and parishin administration reversed the decreased or increased expression of aging-related biomarkers, such as FOXO3a, SIRT1, CASPASE3 and P21, in the gut. ConclusionsThese results suggested that G. elata and parishin could prevent gut aging and ameliorate the “leaky gut” of aged mice and that the underlying mechanism is related to the mutual correlations among barrier function, fecal microbiota composition, and inflammation.

Factors influencing consumer purchase behaviour when buying superfoods

The article provides up-to-date information on consumers’ attitudes towards superfoods. The study analyzed consumers’ behaviours and researched their awareness when deciding to buy superfoods. We explored mutual correlations between selected purchasing factors and their impact on consumer purchase decisions when buying superfoods in Slovenia. We conducted quantitative research, a survey with an online questionnaire (by snowball principle), which has shown that two most important buying factors related to superfoods are consumers’ care for their health and nutritional properties of superfoods, because consumers believe they have a positive effect on health. Our research also indicated that superfoods consumers typically consider imported superfoods to be of better quality than indigenous ones, thus, they are willing to pay a 10-20% higher price for the imported superfoods.