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The fractional Lane–Emden model illustrates different phenomena in astrophysics and mathematical physics. This paper involves the Vieta–Lucas (Vt-L) bases to solve types of variable-order (V-O) fractional Lane–Emden equation (linear and nonlinear). The operational matrix of the V-O fractional derivative is obtained for the Vt-L polynomials. In the established approach, these polynomials are applied to transform the main problem into an algebraic equations system. To indicate the performance and capability of the scheme, a number of examples are presented for various types of V-O fractional Lane–Emden equations. Also, for one example, a comparison is done between the calculated results by our technique and those obtained via the Bernoulli polynomials. Overall, this paper introduces a new methodology for solving V-O fractional Lane–Emden equations using Vt-L bases. The derived operational matrix and the transformation to an algebraic equation system offer practical advantages in solving these equations efficiently. The presented examples and comparative analysis highlight the effectiveness and validity of the proposed technique, contributing to the understanding and advancement of fractional Lane–Emden models in astrophysics and mathematical physics.

Maxwell model is one of the most outstanding and widely used models for the description of viscoelastic materials. In this study, we use an efficient meshfree technique based on the Moving Kriging (MK) interpolation for the numerical solution of Magnetohydrodynamic (MHD) flow of fractional Maxwell fluid. In this scheme to discretize this equation in time and space variables, we use the finite difference method and MK interpolation shape functions, respectively. Also, we calculate the local weak form for every node instead of the computation of the global weak form for the global domain. So, we reduce such problems to a system of algebraic equations. To indicate the efficiency of the present scheme, four examples are discussed in various types of domains and with uniform and nonuniform nodal distribution in 2D cases. Also, to show the validity of the method in this example, a comparison with a valid method has been made. Moreover, in the last example, the accuracy of our scheme in the 3D case is illustrated for the fractional telegraph equation.

Supply chains that are meant to survive must be able to cope with disruptions. The COVID-19 pandemic was one of the most important disruptions that could cause a ripple effect in the supply chain. A ripple effect refers to a disruption that affects other parts of the supply chain like a domino effect, and while the probability of its occurrence is low, its impact is high. This study aims to identify the key success factors for coping with the ripple effect in Iran's machine-made carpet supply chain. In this research, the critical success factors were identified based on in-depth interviews. Then, using the fuzzy cognitive map approach, a model of the critical success factors was created. The statistical population of this research consists of experts in the machine-made carpet industry. The findings showed that there are 23 critical success factors to cope with the ripple effect in the machine-made carpet supply chain. Among them, "supply chain digitization", "capable and efficient managers", and "supply chain collaboration" were identified as the most essential influencing factors, while "supply chain collaboration", "proper planning", and "flexibility in production" were identified as the most important central factors that require special attention. Therefore, policymakers are recommended to focus on enhancing these areas by promoting business intelligence and supply chain management, making universities more mission-oriented, organizing empowerment workshops for managers, and removing supply chain behavioral barriers.

Due to the fact that excellence models, especially EFQM, work based on RADAR logic, which makes them heavily rely on subjective judgments, the aim of this study is to present a more accurate and objective way of assessing organizations with EFQM using Neutrosophic logic approach to minimize estimation errors of assessment values. In this study, assessing organizations based on the EFQM excellence model is performed using Neutrosophic logic, which is a three-valued logic useful in studying uncertainty. First, an assessing tool is designed for evaluation with Neutrosophic logic. Its validity of structure and content was confirmed using EFQM model framework and experts' opinion of evaluation. Also, the reliability was supported in a similar manner using experts' views. Then, the selected organizations, including three state-owned, private, and semi-public banks, were assessed with the assessing tool, and finally, the results of assessing with RADAR logic and Neutrosophic logic were compared. Based on assessing experts of EFQM National Excellence Award and calculations, assessing studied organizations with Neutrosophic logic approach proved more accurate than assessing with RADAR logic approach.

Nowadays, Bitcoin is one of the most important cryptocurrencies that has the largest volume of exchanges in the cryptocurrency market and between businesses. The feature of the possibility of online payments between individuals and businesses directly and without referring to the financial institution has made the price of these cryptocurrencies important for businesses and traders and the basis for decision making. Therefore, the issue of price predictability is an important issue that can be affected by search volume. The purpose of this research is studying and investigating the relationship between the volume of Internet searches and its effect on the price of these cryptocurrencies. In addition, another goal of this article is to introduce Google Trends (GT) as a tool for accessing big data for business researches. The required data was extracted from Google Trends in the period 2016 to 2021. The volume of data was 5742 and the whole statistical population was used. The research method is descriptive-exploratory with the aim of explaining the relationship between "Google search volume index" and "bitcoin price". Data were analyzed using Spearman correlation test. Findings indicate a strong and very strong relationship between the studied indicators, which is explained.

Maxwell model is one of the most outstanding and widely used models for the description of viscoelastic materials. In this study, we use an efficient meshfree technique based on the Moving Kriging (MK) interpolation for the numerical solution of Magnetohydrodynamic (MHD) flow of fractional Maxwell fluid. In this scheme to discretize this equation in time and space variables, we use the finite difference method and MK interpolation shape functions, respectively. Also, we calculate the local weak form for every node instead of the computation of the global weak form for the global domain. So, we reduce such problems to a system of algebraic equations. To indicate the efficiency of the present scheme, four examples are discussed in various types of domains and with uniform and nonuniform nodal distribution in 2D cases. Also, to show the validity of the method in this example, a comparison with a valid method has been made. Moreover, in the last example, the accuracy of our scheme in the 3D case is illustrated for the fractional telegraph equation. [ABSTRACT FROM AUTHOR]

The fractional Lane–Emden model illustrates different phenomena in astrophysics and mathematical physics. This pafa involves the Vieta–Lucas (Vt-L) bases to solve types of variable-order (V-O) fractional Lane–Emden equation (linear and nonlinear). The ofaational matrix of the V-O fractional derivative is obtained for the Vt-L polynomials. In the established approach, these polynomials are applied to transform the main problem into an algebraic equations system. To indicate the faformance and capability of the scheme, a number of examples are presented for various types of V-O fractional Lane–Emden equations. Also, for one example, a comparison is done between the calculated results by our technique and those obtained via the Bernoulli polynomials. Overall, this pafa introduces a new methodology for solving V-O fractional Lane–Emden equations using Vt-L bases. The derived ofaational matrix and the transformation to an algebraic equation system offer practical advantages in solving these equations efficiently. The presented examples and comparative analysis highlight the effectiveness and validity of the proposed technique, contributing to the understanding and advancement of fractional Lane–Emden models in astrophysics and mathematical physics. [ABSTRACT FROM AUTHOR]

Survival analysis, traditionally dominated by models like the Cox Proportional Hazards, faces significant challenges in handling high-dimensional and complex datasets common in modern medical and biological research. This paper introduces an innovative approach to address these challenges by leveraging Variational Autoencoders (VAE) for feature extraction before survival analysis. We propose a framework where VAE compress high-dimensional input data into a lower-dimensional, yet informative latent space. Also, we identify key factors associated with mortality. We use publicly available Covid-19 data and analyze the survival characteristics of a large sample of 566,602 patients. Various machine learning techniques compared to improve prediction accuracy. The performance of the models has been evaluated using metrics like the concordance index and accuracy. The proposed algorithm performs the best with an accuracy of 0.94. Also, it shows good concordance, with training and test concordance index values of 0.91559 and 0.91299, respectively, while other algorithms range from 70% to 91% accuracy. The findings suggest that most features have a significant impact on reducing mortality, except for COPD, asthma, inmsupr, cardiovascular, obesity, renal chronic and tobacco. This model can help identify patients at high risk of death, so they can be prioritized for critical care. [ABSTRACT FROM AUTHOR]

The Restricted Mean Survival Time (RMST) serves as a valuable and extensively utilized metric in clinical trials. However, its application becomes intricate when dealing with data atiected by lengthbiased sampling, rendering traditional inference strategies inadequate. To overcome this challenge, we advocate for the adoption of nonparametric techniques. One notably promising approach is the Empirical Likelihood (EL) method, which furnishes robust results without the need for stringent parametric assumptions. In practical scenarios, the underlying sampling distributions often remain elusive, necessitating adjustments in the case of parametric methodologies. The EL method has demonstrated its eficacy in addressing such complexities. Consequently, this paper introduces the EL method for computing RMST in situations involving both length-biased and right-censored data. Additionally, we introduce the concept of adjusted empirical likelihood (AEL) to further enhance the coverage probability, particularly when dealing with smaller sample sizes. To gauge the performance of the EL and AEL methods, we conduct simulations and rigorously compare their results. The findings unequivocally demonstrate that AEL-based confidence intervals consistently provide superior coverage probability when juxtaposed with EL-based intervals. Lastly, we substantiate the practical applicability of our proposed method by employing it in the analysis of a real dataset. [ABSTRACT FROM AUTHOR]

‎In this article‎, ‎an efficient numerical technique for solving the two-dimensional time-dependent Schrodinger equation is presented‎. ‎At first‎, ‎we employ the meshless‎‎local Petrov-Galerkin (MLPG) method based on a local weak formulation to construct a system of discretized equations and then the solution of time-dependentSchrodinger‎equation will be approximated‎. ‎We use the Moving Kriging (MK) interpolation instead‎‎of Moving least Square (MLS) approximation to construct the MLPG shape functions‎‎and hence the Heaviside step function is chosen to be the test function‎. ‎In this method‎,‎no mesh is needed neither for integration of the local weak form nor construction of the‎‎shape functions‎. ‎So‎, ‎the MLPG is truly a meshless method‎. ‎Several numerical examples‎‎are presented and the results are compared to their analytical and RBF‎‎solutions to illustrate the accuracy and capability of this algorithm‎.

For comprehensive analysis of employees` personality outcomes it is necessary to consider the role of situational factors. The purpose of present study is to investigate the influence of proactive personality on employees` voice behavior in which the moderating role of perceived organizational support as well as job characteristics have been considered. These relationships have been explored within the Telecommunication Infrastructure Company in Tehran with a descriptive-survey approach. Data was collected through questionnaire and was analyzed via inferential statistical techniques as well as structural equation modeling by SPSS and AMOS softwares. Findings show the effect of proactive personality on employees` voice behavior. Meanwhile, the moderating effects of perceived organizational support and job characteristics were supported so that as the positive perceptions of employees` about job characteristics and organizational support increase, the effect of proactive personality on voice behavior increases as well. Also, the moderating role of perceptions about job characteristics was greater than the moderating role of perceived organizational support.

Project portfolio management is a comprehensive framework for decision-making and selecting projects to attain organizational goals by considering resource constraints. The importance of this issue in Iran's oil and gas industry is even more remarkable than ever. This significance is due to its unique position in the country's economy and capital-intensive and capital budget constraints that have intensified recently. Identifying and defining different scenarios for each oil and gas field, determining the parameters of the mathematical model, determining the required data to calculate the model parameters, and identifying the process and methods of identifying this data indicate the distinction and necessity of this research. This study is applied research in terms of objective, using a mathematical modeling approach, which has provided a pattern to determine the optimal portfolio of capital plans of oil and gas companies. The research approach is a case study that has studied one of the country's most important oil and gas-producing companies and the only offshore company. It determines a framework for selecting the optimal portfolio of capital projects. After gathering the required data, the zero-one integer linear mathematical programming model with the objective function of maximizing the net present value from fields (as the company's strategic goal) by considering investment constraints is designed and solved by GAMS software. Finally, the best investment mode for each field and the optimal portfolio are defined according to the defined constraint. [ABSTRACT FROM AUTHOR]

Some goodness-of-fit tests for interval censored data are proposed. For this aim, we introduce a divergence measure based on Kullback–Leibler divergence and use a resampling method called the leveraged bootstrap. We show that these tests are asymptotically consistent. Some results are determined using Monte Carlo simulation, and an example from AIDS research is considered. [ABSTRACT FROM AUTHOR]

In this paper, weconsider the problem of estimating the unknown parameters of an exponentiatedWeibull distribution when the data are observed in the presence of progressively Type II censoring. We observed that the maximum likelihood estimators do not have a closed form, and so require a numerical technique to compute, further the implementation of the EM algorithm still requires the numerical techniques. So we employ the stochastic expectation-maximization (SEM) algorithm to estimate the model parameters and further to construct the associated asymptotic confidence intervals of the unknown parameters. Moreover, under Bayesian approach, we consider symmetric and asymmetric loss functions and compute the Bayesian estimates using the Lindley's approximation and Gibbs sampler together with Metropolis Hastings algorithm. The highest posterior density (HPD) credible intervals are also constructed. The behavior of suggested estimators is assessed using a simulation study. Finally, a real life example is considered to illustrate the application and development of the inference methods. [ABSTRACT FROM AUTHOR]