Importance of bioconvection flow on tangent hyperbolic nanofluid with entropy minimization

The amalgamation of microorganisms in the nanofluid is significant in beautifying the thermal conductivity of several systems, such as microfluid devices, chip-shaped microdevices, and enzyme biosensors. The current investigation studies mixed convective flow of the entropy minimization of unsteady MHD tangent hyperbolic nanoliquid because a stretching surface has motile density via convective and slip conditions. For the novelty of this work, the variable transport characteristics caused by dynamic viscosity, thermal conductivity, nanoparticle mass permeability, and microbial organism diffusivity are considered. It is considered that the vertical sheet studying the flow. By using the appropriate alteration, the governing equations for the most recent flow analysis were altered into a non-dimension relation. Through MATLAB Software bvp4c, the PDE model equations have been made for these transformed equations. Engineering-relevant quantities against various physical variables include force friction, Nusselt number, Sherwood number, and microorganism profiles. The results showed good consistency compared to the current literature. Moreover, these outcomes revealed that augmentation in the magnitude of the magnetic field and velocity slip parameter declines the velocity profile. The reverse impact is studied in We. In addition, heat transfer is typically improved by the influence of thermal radiation parameters, Brownian movement, and thermophoretic force. The physical interpretation has existed through graphical and tabular explanations

Bayesian Statistics Application on Reliability Prediction and Analysis

Reliability predictions focus on developing the appropriate reliability model suitable for existing data. A reliability assessment comes not only from testing the product itself but it is affected by information which is available prior to the start of the test. Bayesian methods are considered efficient in the reliability modeling field when the use of fault trees and reliability diagrams are not possible. Bayes augment likelihood methods with prior information. Bayesian methods are capable of using a variety of information sources: statistical data, expert opinions, historical information, etc. to reach a probability distribution that is used to describe the prior beliefs about the parameter or set of parameters under study. This paper introduces a comprehensive review of using Bayesian network approach for modeling reliability and different methods and statistical distributions used in systems reliability studies

Statistical Modeling and forecasting of weather Data Distribution Using Improved Time Series Analysis

The current study is intended to investigate the applicability of a special class of time series models; autoregressive integrated moving-average (ARMIA) for the estimation of temperature distribution forecast model. Different transformations of ARMIA models such as differencing and smoothing are investigated, in addition to study the effect of each model parameters on the accuracy of the derived model. This study is applied at a temperature time series data of Riyadh city in KSA. By investigating a number of smoothing techniques, simple exponential smoothing (with = 0.2) is found to be the most adequate forecasting model for the case under study as it yields highest correlation factor (R2= 0.9337)

Asymmetric Probability Mass Function for Count Data Based on the Binomial Technique: Synthesis and Analysis with Inference

In this article, a new probability mass function for count data is proposed based on the binomial technique. After introducing the methodology of the newly model, some of its distributional characteristics are discussed in-detail. It is found that the newly model has explicit mathematical expressions for its statistical and reliability properties, which is not the case with many well-known discrete models. Moreover, it can be used as an effectively probability tool for modeling asymmetric over-dispersed data with leptokurtic shapes. The parameters estimation through the classical point of view have been done via utilizing the technique of maximum likelihood and Bayesian approaches. A MCMC simulation study is carried out to examine the performance of the estimators. Finally, two distinct real data sets are analyzed to prove the flexibility and notability of the newly model

Reliability Models to Predict Engineering Systems 'Failures and Improve their Performance

Abstract-In electronic devices systems, the average lifetime could be expected based on handling enormous populations of components probabilistically. Time dependency of reliability was understood by the description, and therefore the time variables appeared in all of the failure distribution purposes that would be consequently. The research will be a specified test system or operating conditions that are necessary to predict reliability. At start of study COMSOL Multiphysics Modeling Software to solve model which indicated the effect of higher temperature by climate or run on the electronic chip. To demonstrate the reason, which may cause the collapse of the device, especially in the country where the climate was hot at certain times of the year such as one of the reasons public and which accelerate the collapse of the systems. Electronic devices had a big difference when tested under the influence of temperature. Significant technical terms related to engineering reliability and having different significances were taking into consideration such as availability, maintainability, and survivability. Probability of collapse was a measured of the reform components will work for a while. Collapse consists of transition from reliability to the case of failure. Regardless of specific mechanism, failure almost always starts off by the movement of time independent of ions and atoms or electronic charge from the benign side to the harmful side. The main problem of the research was how to evaluate a good case for electronic devices operating system by estimating reliability. Data analyzed with support Weibull++/ALTA 9 and BlockSim 9 software and algorithms reliability. Sample reliability with the appropriate had been estimated input parameters for this models (such as failure rates for non-specific situation or event and at the same time to reform the system for the failure of a particular). To provide a system (or part) to estimate the level of reliability of the output parameters (Availability of the system or frequency specific functional failure). Some probability distributions had been presenting such as Weibull, Gumbel and gamma .A comparison between the different distributions to ensure the effectiveness of one of them to determine the reliability of engineering and electronics systems would be derived authority functions and features of the distribution of thermal stress also. Finally cost factors was used to estimate the target reliability for a product and calculate the return on an investment intended to influence that reliability

Exponentiated Generalized Inverted Gompertz Distribution: Properties and Estimation Methods with Applications to Symmetric and Asymmetric Data

In this article, a new four-parameter lifetime model called the exponentiated generalized inverted Gompertz distribution is studied and proposed. The newly proposed distribution is able to model the lifetimes with upside-down bathtub-shaped hazard rates and is suitable for describing the negative and positive skewness. A detailed description of some various properties of this model, including the reliability function, hazard rate function, quantile function, and median, mode, moments, moment generating function, entropies, kurtosis, and skewness, mean waiting lifetime, and others are presented. The parameters of the studied model are appreciated using four various estimation methods, the maximum likelihood, least squares, weighted least squares, and Cramér-von Mises methods. A simulation study is carried out to examine the performance of the new model estimators based on the four estimation methods using the mean squared errors (MSEs) and the bias estimates. The flexibility of the proposed model is clarified by studying four different engineering applications to symmetric and asymmetric data, and it is found that this model is more flexible and works quite well for modeling these data

Bio-convection Eyring-Powell nanofluid through a spinning disk with a heated convective stretching sheet

Recent research has linked the improvement of extrusion features, rotatary heat process and biofuel production to the use of nanoparticles. The prime proposes of the current scrutinization is to inspect the MHD flow of Powell-Eyring fluid induce by the nanofluid and bioconvection flow through a spinning disk. Together with nanofluids, a blend of bioconvective is employed to improve the system's thermal performance which has applications in different technological systems. The flow is considered over a stretable spinning disk. The Buongiorno model has been produced to adequately reflect how the role of nanoliquid affects Brownian motion and thermophoresis characteristics. Arrhenius's activation energy has also been considered. By applying the suitable transformation, the obtained boundary layer expressions are altered into a set of ordinary differential expressions. The three stages Lobatto BVP4c technique is utilized for ordinary differential expressions. The influences of pertinent variables on different profiles are represented graphic form. The study demonstrates tangential and axial velocity reduces with larger magnitude of Ma as the opposite effect is noted for fluid parameters α1. Moreover, the concentration distributions enhance with rising values of κ and Ea . The results are calculated using previously published research, and excellent conformity is discovered

Bayesian and Frequentist Approaches for a Tractable Parametric General Class of Hazard-Based Regression Models: An Application to Oncology Data

In this study, we consider a general, flexible, parametric hazard-based regression model for censored lifetime data with covariates and term it the “general hazard (GH)” regression model. Some well-known models, such as the accelerated failure time (AFT), and the proportional hazard (PH) models, as well as the accelerated hazard (AH) model accounting for crossed survival curves, are sub-classes of this general hazard model. In the proposed class of hazard-based regression models, a covariate’s effect is identified as having two distinct components, namely a relative hazard ratio and a time-scale change on hazard progression. The new approach is more adaptive to modelling lifetime data and could give more accurate survival forecasts. The nested structure that includes the AFT, AH, and PH models in the general hazard model may offer a numerical tool for identifying which of them is most appropriate for a certain dataset. In this study, we propose a method for applying these various parametric hazard-based regression models that is based on a tractable parametric distribution for the baseline hazard, known as the generalized log-logistic (GLL) distribution. This distribution is closed under all the PH, AH, and AFT frameworks and can incorporate all of the basic hazard rate shapes of interest in practice, such as decreasing, constant, increasing, V-shaped, unimodal, and J-shaped hazard rates. The Bayesian and frequentist approaches were used to estimate the model parameters. Comprehensive simulation studies were used to evaluate the performance of the proposed model’s estimators and its nested structure. A right-censored cancer dataset is used to illustrate the application of the proposed approach. The proposed model performs well on both real and simulation datasets, demonstrating the importance of developing a flexible parametric general class of hazard-based regression models with both time-independent and time-dependent covariates for evaluating the hazard function and hazard ratio over time

A New Flexible Univariate and Bivariate Family of Distributions for Unit Interval (0, 1)

We propose a new generator for unit interval which is used to establish univariate and bivariate families of distributions. The univariate family can serve as an alternate to the Kumaraswamy-G univariate family proposed earlier by Cordeiro and de-Castro in 2011. Further, the new generator can also be used to develop more alternate univariate and bivariate G-classes such as beta-G, McDonald-G, Topp-Leone-G, Marshall-Olkin-G and Transmuted-G for support (0, 1). Some structural properties of the univariate family are derived and the estimation of parameters is dealt. The properties of a special model of this new univariate family called a New Kumaraswamy-Weibull (NKwW) distribution are obtained and parameter estimation is considered. A Monte Carlo simulation is reported to assess NKwW model parameters. The bivariate extension of the family is proposed and the estimation of parameters is described. The simulation study is also conducted for bivariate model. Finally, the usefulness of the univariate NKwW model is illustrated empirically by means of three real-life data sets on Air Conditioned Failures, Flood and Breaking Strength of Fibers, and one real-life data on UEFA Champion’s League for bivariate model