Variance Criterion Research Articles

Integrating Flow Field Dynamics and Chemical Atmosphere Predictions for Enhanced Sulfur Corrosion Risk Assessment in Power Boilers.

In this work, we attempt to explain the phenomenon of sulfur corrosion of power boiler water walls under the conditions of large fluctuations in carbon monoxide concentrations. To assess the conditions required for corrosion formation, a criterion based on the chemical and flow field parameters of the flue gas is proposed. The formulated sulfur corrosion criterion is based on the mixture fraction variance and the turbulence time scale. Numerical modeling of coal combustion in a 250 MW power boiler is performed using ANSYS. Two cases of combustion in a boiler are analyzed, with the first simulating the boiler operated using classic high-swirl burners and the second one accounting for boiler operation with modified low-swirl burners. Calculations of pulverized coal combustion are performed using the standard k-ε turbulence model and the combustion described by the mixture fraction. The simulation results reveal that the low-swirl burner is characterized by higher values of the mixture fraction variance and a higher frequency of fluctuation of the velocity field, which is strongly related to an increased corrosion rate. The study outcomes show the validity of using the criterion of the mixture fraction variance and velocity field fluctuations to determine the areas at risk of sulfur corrosion.

Методика «Схематизация» в цифровом формате для оценки уровня развития наглядно-образного мышления старших дошкольников

<p>The development of imaginative thinking is one of the main tasks of educational work with preschoolers, because it allows the child to be successful in preschool activities, where the most favorable conditions for the formation of age new achievements are formed. The need for diagnostic tests to determine the level of imaginative thinking of preschoolers is related both to the need to assess the effectiveness of educational work with children, and to identify the level of development of the child. The most informative and reliable results of the assessment of cognitive processes are provided by the use of standardized test. In Russian psychology, there is only one such test - "Schematization" (author R.I. Bardin). This test was created and standardized at the end of the last century and its psychometric characteristics and assessment standards need to be updated for a modern sample. The article presents the results of the re-standarization of this test. The experimental sample of the study consisted of N = 2425 children of senior preschool age: children 5-6 years old (M = 66 months; SD = 3.09 months; n = 974) and 6-7 years old (M = 77 months; SD = 3.21 months; n = 1451). For psychometric verification, confirmatory factor analysis, Cronbach's alpha and McDonald's omega coefficients, Spearman correlation coefficient, Mann-Whitney U criterion and two-factor analysis of variance were used. The norms were calculated using percentile normalization. The obtained results showed the conformity of the test with psychometric requirements and allowed us to determine the current norms for the development of imaginative thinking of older preschool children.</p>

Agricultural sustainability assessment in Fars province of Iran through the lens of the elimination multi-criteria decision-making method

The main objective of the present research was to assess agricultural sustainability through the lens of the Elimination Method. Iranian wheat farmers in Fars province comprised the population of the study. Smapling table of Krejcie and Morgan was applied to estimate the statistically plausible sample size. A stratified sampling approach was used for selecting sampling randomly. The research instrument included a structured questionnaire. A panel of experts approved the face and content validities. Average Variance Extracted (AVE), Composite Reliability (CR), and items correlations were used to confirm the convergent validity of the constructs. The average shared squared variance (ASV) and the maximum shared squared variance (MSV) criteria were applied to examine the divergent validity of constructs. Employing the Elimination Method to assess agricultural sustainability in four counties of Fars revealed that Marvdasht is the most sustainable area for the development of wheat cultivation. Because it was able to fulfill 16 indicators out of 23 indicators of agricultural sustainability. The high level of productivity and efficiency of the wheat crop in this region is one of the main reasons for the relatively high sustainability of wheat production practices. Furthermore, the results demonstrated that Darab was the second most suitable region for developing wheat production. Based on the Elimination Method, the region was able to fulfill six indicators out of 23 agricultural sustainability assessment indicators. According to the results of ranking alternatives using the Elimination Method, the two counties of Kazeroun and Eghlid could not acceptably fulfill indicators of agricultural sustainability. In other words, it seems that these two regions are not suitable for the development of wheat production in Fars province. In the end, it was recommended that the Elimination Method can be a useful approach for urgent decision-making and agricultural managers and decision-makers in Fars province should focus more intensively on the development of wheat production in Marvdasht.

Визначення предикторів діагнозу пацієнтів з ПТСР на основі параметрів одновимірних моделей першого порядку BOLD-сигналів структур мозку та МГУА

Introduction. The use of functional magnetic resonance imaging (fMRI) allows for the assessment of processes occurring in the brain. By analyzing the examination results, it is possible to establish the parameters of connections between brain structures, and changes in the values of these parameters can be used as diagnostic conclusion predictors for PTSD-patients. Purpose. To identify predictors for the classification of the PTSD diagnosis using the connectivity parameters of BOLD signals from brain structures. Methods. The technology for identifying predictors of PTSD diagnosis is based on a) the formation of connectivity parameters of BOLD signals from brain structures obtained during resting-state scanning, b) the use of classifier-oriented selection based on inter-class variance and mRMR criteria to select informative features, and c) the classification of PTSD diagnosis using a logistic regression algorithm optimized by the Group Method of Data Handling. Results. The technology proposed in this work enabled the selection of informative features and the identification of their predictive forms, resulting in the formation of classifiers for the diagnosis of PTSD with high accuracy, sensitivity, and specificity. Conclusion. A technology for the formation, selection, and use of connectivity parameters of BOLD signals from brain structures has been proposed for differentiating healthy individuals from those who suffer with PTSD. A list of the most informative features of PTSD and their predictive forms in the form of generalized variables has been obtained, which can be used for diagnostic conclusions. The results obtained indicate the presence of a specific type of connection between the brain areas identified in the study based on levels of excitation (parameters а0 of the models) and the alteration of these levels in the context of PTSD.

Robust fusion filter for networked uncertain descriptor systems with colored noise and cyber-attacks

The robust fusion filtering problem of multi-sensor networked uncertain descriptor systems (NUDSs) with colored noise, uncertain noise variances and cyber-attacks is investigated. During data transmission in unreliable communication networks, the data can be maliciously attacked by attackers. In other words, the local filters (LFs) may receive false data or may not receive data because of the cyber-attacks. By adopting the singular value decomposition (SVD) method, the original NUDSs can be converted into two reduced-order subsystems with uncertain correlated fictitious white noises, and the cyber-attacks are transformed into the fictitious noises. Cross-covariance matrices between local filtering errors are derived. The robust LFs are obtained according to the minimax robust estimation principle. Under the linear unbiased minimum variance criterion, three weighted fusion algorithms are applied to fuse the LFs. For all allowable uncertainties of noise variances and cyber-attacks, the minimal upper bounds of covariance matrices of the local and distributed fusion filters are guaranteed. The proof of their robustness is established through the minimax estimation principle and Lyapunov equation method. Finally, the correctness and effectiveness of the proposed algorithms are verified by a circuit system example.

Unveiling the gut microbiota blueprint of schizophrenia: a multilevel omics approach.

Schizophrenia is a persistent incurable mental disorder and is characterized by the manifestation of negative emotions and behaviors with anxiety and depression, fear and insecurity, self-harm and social withdrawal. The intricate molecular mechanisms underlying this phenomenon remain largely elusive. Accumulating evidence points towards the gut microbiota exerting an influence on brain function via the gut-brain axis, potentially contributing to the development of schizophrenia. Therefore, the objective of this study is to delineate the gut microbial composition and metabolic profile of fecal samples from individuals with schizophrenia. Liquid chromatography-mass spectrometry (LC-MS) and 16S ribosomal RNA (16S rRNA) gene sequencing were employed to analyze fecal metabolites and gut microbiota profiles in a cohort of 29 patients diagnosed with schizophrenia and 30 normal controls. The microbial composition of fecal samples was determined through the 16S rRNA gene sequencing, and microbial α-diversity and β-diversity indices were calculated. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to analyze the distribution of samples. The metabolites and gut microbiota exhibiting differential expression were identified through the application of biological variance criteria. Co-occurrence analysis of bacteria and metabolites was conducted using the spearman's rank correlation coefficient and visualized in a circular layout with the Cytoscape software. The findings of the study indicated a lack of substantial evidence supporting significant disparities in α-diversity and β-diversity between individuals with schizophrenia and normal controls. In terms of metabolomics, a discernible pattern in sample distribution between the two groups was observed. Our analysis has revealed 30 bacterial species and 45 fecal metabolites that exhibited notable differences in abundance between individuals diagnosed with schizophrenia and normal controls. These alterations in multilevel omics have led to the development of a co-expression network associated with schizophrenia. The perturbed microbial genes and fecal metabolites consistently demonstrated associations with amino acid and lipid metabolism, which play essential roles in regulating the central nervous system. Our results offered profound insights into the impact of imbalanced gut microbiota and metabolism on brain function in individuals with schizophrenia.

Alpha-robust mean–variance reinsurance and investment strategies with transaction costs

This paper investigates the time-consistent reinsurance and investment strategies for insurers based on the alpha-robust mean–variance criterion. We assume that transaction costs with quadratic form exist in the financial market composed of a risk-free asset and n risky assets, and the insurance and financial markets are correlated. By solving a system of extended HJB equations, the equilibrium reinsurance and investment strategy and the corresponding value function are derived in terms of the solution to a system of matrix Riccati equations. In some special cases, more explicit expressions for the equilibrium strategies and value functions are provided. Numerical examples demonstrate that the growth rate of investment slows down as the transaction costs level or the correlation coefficient increases. In addition, we find that the transaction costs level has opposite effects on the utility losses due to ignoring jumps or ambiguity.

An Ideal Four-factor Components Mixture Experiment Design Based on Prominent Optimality Criteria at Level Two

Aim: Mixture experiment designs are essential tools that need to be determined prior to conducting any mixture experiment in any field of study. The primary goal of these types of experiments are optimization that is either maximizing the profit/produce or minimizing the cost of production. Study Design: Any researcher anticipating to do any research work on the mixture experiment will not evade to talk about the design that he or she is likely to use. The common of such designs being either Simplex Lattice Design (SLD) or Simplex Centroid Design (SCD). Methodology: The choice of such design is wholly based on the optimality criteria employed. The classical of these criteria include on D-Determinant criterion, A-Average variance criterion, E- Eigen value criterion and T- Trace optimality criterion usually denoted as D-, A-, E- T- criteria. They are also known as prominent criteria. Results: This paper considered four-factor components at order two. The penalized moment matrices obtained from the information matrices whose primary source was the design points gave the values of the criteria aforementioned. These values were ranked independently with the least average rank termed as the best design. Conclusion: The {4,4} Simplex Lattice Design had the lowest rank value of 1.0 as compared to the other designs. This design is therefore to be used in any research work considering four factors when only two of factors are to be employed.

Credit market conditions, expected return proxies, and bank stock returns

We evaluate the performance of expected return proxies during extreme credit market conditions and extreme phases of business cycles when realized returns on banks stocks are large in absolute value. We construct three sets of expected return proxies for individual bank stocks: (i) characteristic-based proxies; (ii) standard risk-factor-based proxies; and (iii) risk-factor-based proxies in which betas depend on firm characteristics. Based on the newly developed minimum error variance (MEV) criterion (Lee et al., 2020), the best performing expected return proxy is the risk-factor-based model that allows betas to vary with firm characteristics. We also examine whether these three expected return proxies can capture actual returns during either extreme credit market or extreme business-cycle conditions. We find that both risk-factor-based proxies explain returns better than characteristic-based proxies during these periods.

Modeling mispricing risk of defined contribution pension plan with a mean–variance criteria

This manuscript addresses modeling mispricing risk of defined contribution pension plan (DCPP) with a mean–variance criterion to obtain the optimal investment strategy. Provides a way for the sustainability of pensions by investing in the financial market. The pension manager’s objective is to maximize the expected terminal wealth while simultaneously minimizing the associated risk. We employ the stochastic dynamic programming principle (SDPP) and the Lagrange dual theorem to derive the efficient frontier and strategy, then two special cases are examined. Last, we conduct a numerical analysis to show how different parameters influence the efficient frontier and strategy. This analysis sheds light on the economic implications of our findings.

Identification and distributed fusion filter for multi-sensor networked systems with stochastic deception attacks

In the multi-sensor networked systems, each sensor produces its local state estimate based on its own observations, and then sends it to a fusion center (FC) for fusion estimation. During transmitting local state estimates to the fusion center, the data may suffer from stochastic deception attacks from malicious attackers, where the probabilities of attacks and the variances of attack noises are unknown. Using a correlation function method (CFM), the probabilities of attacks and the variances of attack noises in individual channels are identified in parallel. The possibly attacked local state estimate is filtered to reproduce local state estimate in the FC. Using the matrix-weighted fusion algorithm in the linear unbiased minimum variance (LUMV) criterion, a distributed fusion filter is performed based on the reproduced local state estimates. A simulation example illustrates the validity of the algorithms.

Federal Algorithm Design and Numerical Experiments of Ensemble Kalman Filter Data Assimilation Analysis Process

Based on the federated Kalman filter proposed by Carlson with linear systems, we propose a federated computing scheme for the ensemble Kalman filter (EnKF) assimilation analysis process for nonlinear systems, and give an optimal information fusion estimation algorithm weighted by diagonal matrix under the linear minimum variance criterion, that is, the assimilation analysis values of each variable in the global estimation are linear combinations of the assimilation analysis values of the corresponding variables in the local estimation of sub-filters, and the calculation of the combination coefficients is given. The federated algorithm of the EnKF assimilation analysis process for nonlinear systems is verified by the Lorenz (1963) system.

A Test of Dublin Anti-bullying Self-Efficacy Scale for Teachers (DABSE-T)

AbstractEmerging evidence suggests that anti-bullying programmes should have a particular focus on teacher self-efficacy for peer bullying prevention and intervention at schools. To address this suggestion, a theoretical framework and a measurement scale are needed to evaluate teacher anti-bullying self-efficacy and determine its role in the effectiveness of these anti-bullying programmes. The present research aims to adapt the Anti-Bullying Self-Efficacy Theory and test the psychometric properties of the Dublin Anti-Bullying Self-Efficacy Scale (DABSE) for teachers. A convenience sample of 221 teachers (38.9% from primary and 61.1% from post-primary schools) responded to the DABSE-T following participation in FUSE, a school anti-bullying programme in Ireland. The factorial structure of the scale was assessed using Exploratory Factor Analysis (EFA), Principal Axis Factoring with Promax Oblique rotation. Construct validity (convergent and divergent validity) was assessed using the criteria of Average Variance Extracted (AVE > .50) and the heterotrait-monotrait ratio of correlations (HTMT). Composite reliability was estimated as an indicator of internal consistency. The DABSE-T demonstrated satisfactory psychometric properties, suggesting that it is a valid and reliable measure of teacher self-efficacy beliefs, which encompass recognition of bullying behaviour, comprehension of the need for immediate intervention, acceptance of responsibility, knowledge of appropriate actions, and intervention implementation. The scale can be used to evaluate the effectiveness of anti-bullying interventions and identify teachers who may require additional support in addressing bullying incidents.

Role of risk aversion in operational decisions with remanufacturing under emissions price volatility

Previous studies have extensively investigated the optimal operational decisions for firms under emission trading scheme, assuming a constant emissions price. However, the volatility of the emissions price has always been ignored, leaving the resulting operational decisions unclear for risk-averse firms. In this paper, we examine the impacts of risk aversion in the presence of emissions price volatility. Specifically, we investigate a risk-averse firm that strives to reduce associated costs through emissions abatement under emissions price volatility. The firm sells both new and remanufactured products. To illustrate the role of risk aversion, we sequentially examine risk-neutral and risk-averse scenarios using the mean–variance criterion as the risk aversion measurement. Among other results, we demonstrate that the risk-averse firm has incentives not only to remanufacture all used products, but also to increase the remanufactured quantity under certain conditions. Surprisingly, we find that risk aversion may not always hinder investments in emissions reduction. Subsequently, we discuss how risk aversion influences consumers and the environment, using the life cycle analysis-based approach to model environmental impacts. The results indicate that as the degree of risk aversion increases, consumers are worse-off, and there is a constant conflict between economic and environmental objectives. Additionally, it is interesting to find that the emission abatement investment strategy does not necessarily improve environmental benefits.

Optimal portfolio strategy of wealth process: a Lévy process model-based method

This paper is concerned with the optimal portfolio problem for a company that can invest in two risky assets, where a novel Lévy-process-driven model is constructed to describe the dynamics of the wealth process by using a constant elasticity of variance model and a jump-diffusion process. A delicately designed value function is proposed under the mean–variance criterion to reflect the optimal portfolio for the stochastic volatility model. By using the verification theorem, the desired optimal portfolio strategy is proposed by the solution to certain Hamilton–Jacobi–Bellman equations. Furthermore, the corresponding expressions are achieved by using the stochastic analysis theory. Finally, a numerical simulation example is provided to verify the effectiveness of the proposed optimal portfolio strategy.

Research on the Construction of Economic Statistical Model and Application of Data Cleaning Technology in Big Data Environment

Abstract With the rapid development of big data technology, the number of economic data grows faster, and it is more challenging for people to grasp and calculate economic data. In this paper, an economic statistical model is proposed to optimize data-cleaning technology. The economic data application framework is constructed using Multi-Agents, and the mining of economic data is achieved using Microsoft time series and clustering algorithm. The GMDH algorithm, which is the core of self-organized data mining, is proposed and improved to complete economic data analysis and prediction by using the fitting error or prediction variance criterion as the identification criterion. The wild value identification method based on the regression model is utilized to eliminate potential wild values and carry out data cleaning. Statistical analysis of the economic data of the printing industry in Shanghai, China, reveals that the total assets, total industrial output value, and total profit of the printing industry in Shanghai in 2023 declined by 2.63%, 4.77%, and 5.68%, respectively. External investment and enterprise R & D investment up to 109,440,100 U.S. dollars, 140,301,000 yuan. The overall number of employees declined, and the profit margin on output value decreased by 5.68%. It is predicted that the number of enterprises, total assets, total industrial output value, and total profit of Shanghai’s printing industry will rebound in 2024, and the external direct investment and R&D investment will be appropriately reduced.

Maximizing the profit of omnichannel closed-loop supply chains with mean–variance criteria

The maximization of profitability stands as a paramount objective within supply chains. The conventional approach involves implementing an omnichannel retailing strategy to integrate diverse sales channels. However, such implementation often introduces uncertainty in product pricing and recycling, undermining the system’s robustness. In this study, we leverage the combined power of leader-follower game and mean–variance theories to construct two optimization models for the omnichannel closed-loop supply chain. Specifically, our focus lies in analyzing the performance of manufacturers who distribute products through physical stores. By meticulously considering the risk aversion attribute inherent in the supply chain, we derive closed-form solutions for the optimal approach. The main results show the positive impact of a risk-averse attitude exhibited by the physical store on the overall system profitability. However, if the introduced physical store belongs to another firm, then total profit experiences a decline. To further substantiate our claims, we conduct a comprehensive numerical analysis to show the performance of our optimization models.

Variability of photosynthetic parameters of macroalgae and seagrasses based on laboratory experiments

The PE-curve describes dependence of the macroalgae and seagrasses photosynthesis rate on the irradiance. To determine the main parameters of the PE-curve (photosynthetic parameters) at various temperatures and light intensities, an archive of laboratory experiments data was collected from the available published sources, and systematized (a total of 125 species of macroalgae and 14 species of seagrasses). The available data on the ratio of plant surface area to volume (SA:V) were added to the database. All macroalgae species were assigned to functional-form groups according to Littler [15], and an additional group “seagrasses” was determined.The data on photosynthetic parameters of macrophytobenthos were clusterized using the Ward's minimum variance criterion, which made it possible to identify clusters that differ greatly in the rate of photosynthesis. The composition of each cluster was determined within the framework of the classification by functional-form groups. The variability of functional parameters was studied by calculating the coefficients of variation within a functional-form group, cluster, and taxonomic division in a narrow temperature range.Graphs of the oxygen daily production by plants were plotted for different water temperatures to determine the temperature dependence of the photosynthesis rate. Using the method of constructing a neural network, the regression equations for the photosynthetic parameters were found in the form of polynomial dependencies on the morphological characteristics of algae: the SA:V ratio and the Littler group. With limited prognostic value, they illustrate well the qualitative changes in photosynthetic parameters during the transition from thin sheet-like and filamentous structures of thalli to coarser and more resistant ones.

Neighborhood component analysis to leverage the class label information during feature selection to enhance the damage classification performance

In the health monitoring of large structures data from multiple sensors are fused to get reliable and robust results. Using data from multiple sensors leads to high dimensional feature vectors which result in computational inefficiency and overfitting. Therefore, it is necessary to reduce the dimension of these feature vectors, especially in the case of real-time health monitoring systems. Principal component analysis has become a go-to algorithm for dimensionality reduction in the structural health monitoring literature. However, it reduces the dimension by maximum variance criterion without considering the class labels of these initial feature vectors, this implies that the reduced dimensional feature vectors do not guarantee best class separability in damage classification problems. Using linear discriminant analysis to solve this issue poses singularity problems in the damage classification problems with small datasets having high dimensional feature vectors. This paper proposes the use of neighborhood component analysis for reducing the dimensionality of the feature vectors. As in the case of damage type identification problems the input signals have to be labeled, neighborhood component analysis makes use of the class label information and reduces the dimension by considering the classification performance. The algorithm’s performance has been validated by solving a damage classification problem by combining it with a K-nearest neighbor classifier. An experimental benchmark dataset from the American Society of Civil Engineers is utilized for this purpose. A comparison with the Principal Component Analysis on the same dataset depicts that the ability of Neighborhood Component Analysis to include the class label information indeed helps in enhancing the damage classification performance.

Mean–variance value at risk criterion for solving a p-median location problem on networks with type-2 intuitionistic fuzzy weights

In this paper, we consider the p-median location problem on networks in which the vertex weights are the type-2 intuitionistic fuzzy variables. First, the credibilistic value at risk of the intuitionistic fuzzy variables is introduced. Using value at risk criterion, we define a new method for reducing of type-2 intuitionistic fuzzy variables to intuitionistic fuzzy variables that is named the value at risk reduced intuitionistic fuzzy variables. Further, the mean and variance values of value at risk reduced intuitionistic fuzzy variables are described. Finally, we apply the mean–variance criterion for solving the type-2 intuitionistic fuzzy p-median location problem. Also, it is shown that this problem is equivalent to a parametric quadratic convex programming problem.