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Abstract A novel two-parameter continuous model titled the entropy-transformed Weibull (ET-W) distribution has been developed via the entropy transformation. A new framework has been investigated and found to meet the criteria of the probability function. By significantly improving the functional shape and having the ability to model the most likely form of the hazard rate function, this novel modification has increased the adaptability of typical model. Some of its core characteristics, such as its statistical and computational features, are simply and clearly presented. To examine the ultimate performance of maximum likelihood estimators during the process of estimating model parameters, a comprehensive simulation analysis has been conducted. The effectiveness of the suggested distribution is illustrated through the modeling of real datasets.

The bivariate FGM Entropy-Transformed model is a new, flexible family of generalized bivariate distributions based on the FGM copula. The bivariate FGM Entropy-Transformed Weibull model is a new model that we developed based on the FGM copula and Entropy-Transformation. A number of structural statistical properties, such as marginals, conditional distribution, conditional expectation, product moments, moment generating function, reliability function, and hazard rate function, have been inferred from our proposed bivariate family. It is possible to determine the parameters of the model using the maximum likelihood estimation method. Asymptotic confidence intervals are developed as well for the parameter model. We conduct a comprehensive evaluation of the proposed bivariate distribution by performing an extensive Monte Carlo simulation. Finally, the data set analysis demonstrates the practical applicability of the proposed bivariate family.

Chemically reactive squeezing flows play a crucial role in various industrial and engineering processes, such as in polymer manufacturing, chemical reactors, and biofluid mechanics, where precise control over fluid behavior is essential for optimizing product quality and efficiency. Current study has focused on the chemically reactive squeezing fluid flow through a non-Darcy medium due to a stretching surface, employing response surface methodology. This research investigates the heat and mass transfer processes under convective conditions and non-linear stratification, with an emphasis on enhancing heat transfer through the incorporation of radiation effects. The non-linear governing system is solved numerically using the Runge–Kutta fourth-order method (RK-4) combined with the shooting technique. Critical parameter variations are illustrated through graphical representations, highlighting their impact on the relevant fields. The Nusselt number, Sherwood number, and skin friction coefficient are computed numerically for various parameter settings. Additionally, a comprehensive statistical analysis is conducted, utilizing the correlation coefficient and probable error to assess the influence of governing input variables on the output parameters of interest. The study also includes a sensitivity analysis, revealing that the sensitivities of the heat transport rate to the radiation parameter and thermal stratification parameter increase with higher Biot numbers. The high R2 and R2−adj values, being close to unity, indicate that the proposed model demonstrates a robust empirical relationship between the responses and independent factors for both local Nusselt and Sherwood numbers.

From the last few years power demanding is increasing as per load requirement. The condition of power demand is not accompanied by only generation and transmission of electricity. Many industrial power plants are running on higher capacity to fulfill load requirement. There are many issues such as voltage instability and power quality which led to many power plants. In distributed generation generators are distributed asynchronous type and mostly affected by the reactive power. They basically cannot generate reactive power but draws away from the power system. It causes the voltage drop to the consumer side which may be not acceptable. Then reactive power management is essential for reducing those problems and provides continuity of electricity to the consumers. Distributed generation systems and FACTS devices are converting conventional power grid into smart grid. Reactive power compensation is now challenging issue to preserve adequate power quality and improve the performance of distribution system. There are many FACTS devices such as we can control power factor of the circuit and reactive power compensation. Many researchers have focused on behavior of FACTS device in an unbalanced condition and majority work is done only with DSTATCOM. This paper deals with different FACTS devices in grid systems with analysis and overview of D-FACTS devices. Hence in this paper, applications of various D-FACTS devices have been described to find location of compensation devices for reliable operation.

When software systems are introduced, they are typically deployed in field environments similar to those used during development and testing. However, these systems may also be used in various other locations with different environmental conditions, making it challenging to improve software reliability. Factors such as the specific operating environment and the location of bugs in the code contribute to this difficulty. In this paper, we propose a new software reliability model that accounts for the uncertainty of operating environments. We present the explicit closed-form mean value function solution for the proposed model. The model's goodness of fit is demonstrated by comparing it to the nonhomogeneous Poisson process (NHPP) model based on Weibull model, using four sets of failure data sets from software applications. The proposed model performs well under various estimation techniques, making it a versatile tool for practitioners and researchers alike. The proposed model outperforms other existing NHPP Weibull based in terms of fitting accuracy under two different methods of estimation and provides a more detailed and precise evaluation of software reliability. Additionally, sensitivity analysis shows that the parameters of the suggested distribution significantly impact the mean value function.

In survival and stochastic lifespan modeling, numerous families of distributions are sometimes considered unnatural, unjustifiable theoretically, and occasionally superfluous. Here, a novel parsimonious survival model is developed using the Bilal distribution (BD) and the Kavya-Manoharan (KM) parsimonious transformation family. In addition to other analytical properties, the forms of probability density function (PDF) and behavior of the distributions' hazard rates are analyzed. The insights are theoretical as well as practical. Theoretically, we offer explicit equations for the single and product moments of order statistics from Kavya-Manoharan Bilal Distribution. Practically, maximum likelihood (ML) technique, which is based on simple random sampling (SRS) and ranked set sampling (RSS) sample schemes, is employed to estimate the parameters. Numerical simulations are used as the primary methodology to compare the various sampling techniques.

The core objective of this research is to describe the behavior of the distribution using the MLE method to estimate its parameters, as well as to determine the optimal Artificial Neural Network method by comparing it to the maximum likelihood estimation method and applying it to real data for breast cancer patients to determine survival, risk, and other survival study functions of the log-logistic distribution. The parameters were defined in the input layer of the artificial neural network developed for the purpose of survival analysis and reliability function, hazard rate function, probability density function, reserved hazard rate function, Mills ratio, Odd function and CHR values were obtained in the output layer. The findings show that risk function increases with the increase in the time of infection and then decreases for a group of breast cancer patients under study, which corresponds to the theoretical properties of this according to the practical conclusions. The examination of survival analysis reveals that practical conclusions correspond to the theoretical properties of log-logistic distribution. Artificial neural networks have proven to be one of the ideal tools that can be used to predict various vital parameters, especially survival of cancer patients, with their high predictive capabilities.

The mathematical characteristics of the mixture of Lindley model with 2-component (2-CMLM) are discussed. In this paper, we investigate both the practical and theoretical aspects of the 2-CMLM. We investigate several statistical features of the mixed model like probability generating function, cumulants, characteristic function, factorial moment generating function, mean time to failure, Mills Ratio, mean residual life. The density, hazard rate functions, mean, coefficient of variation, skewness, and kurtosis are all shown graphically. Furthermore, we use appropriate approaches such as maximum likelihood, least square and weighted least square methods to estimate the pertinent parameters of the mixture model. We use a simulation study to assess the performance of suggested methods. Eventually, modelling COVID-19 patient data demonstrates the effectiveness and utility of the 2-CMLM. The proposed model outperformed the two component mixture of exponential model as well as two component mixture of Weibull model in practical applications, indicating that it is a good candidate distribution for modelling COVID-19 and other related data sets.

In explaining and forecasting real life scenarios, statistical distributions are very helpful. It is very important to select the best fitting statistical distribution for modelling data. In analysis of real world phenomena like in reliability and economics, we may find distributions for bounded data observed as percentages, proportions or fractions (see, for example, Marshall and Olkin (2007)). In this context, in view of pertinent transformation on the Gumbel Type-II model, we suggest and study the unit Gumbel Type-II (UG-TII) model and explore few of its statistical characteristics. We also consider various methods of estimating the unknown parameters of UG-TII model from the frequentist perspective. Monte Carlo simulations are worked out in order to compare efficiency of suggested estimation methods for small as well as large samples. The efficiency of estimators is measured using simulated samples in terms of their bias and mean square error. In the end, two datasets have been examined in attempt to validate the realistic possibilities of new model. In comparison to the six severe competitors.

Abstract This article presents the implementation of a numerical solution of bioconvective nanofluid flow. The boundary layer flow (BLF) towards a vertical exponentially stretching plate with combination of heat and mass transfer rate in tangent hyperbolic nanofluid containing microorganisms. We have introduced zero mass flux condition to achieve physically realistic outcomes. Analysis is conducted with magnetic field phenomenon. By using similarity variables, the partial differential equation which governs the said model was converted into a nonlinear ordinary differential equation, and numerical results are achieved by applying the shooting technique. The paper describes and addresses all numerical outcomes, such as for the Skin friction coefficients (SFC), local density of motile microorganisams (LDMM) and the local number Nusselt (LNN). Furthermore, the effects of the buoyancy force number, bioconvection Lewis parameter, bioconvection Rayleigh number, bioconvection Pecelt parameter, thermophoresis and Brownian motion are discussed. The outcomes of the study ensure that the stretched surface has a unique solution: as Nr (Lb) and Rb (Pe) increase, the drag force (mass transfer rate) increases respectively. Furthermore, for least values of Nb and all the values of Nt under consideration the rate of heat transfer upsurges. The data of SFC, LNN, and LDMM have been tested utilizing various statistical models, and it is noted that data sets for SFC and LDMM fit the Weibull model for different values of Nr and Lb respectively. On the other hand, Frechet distribution fits well for LNN data set for various values of Nt.

Abstract In current investigation, a novel implementation of intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural networks (ANN) with the Levenberg–Marquard algorithm is provided to interpret heat generation/absorption and radiation phenomenon in unsteady electrically conducting Williamson liquid flow along porous stretching surface. Heat phenomenon is investigated by taking convective boundary condition along with both velocity and thermal slip phenomena. The original nonlinear coupled PDEs representing the fluidic model are transformed to an analogous nonlinear ODEs system via incorporating appropriate transformations. A data set for proposed MLP-ANN is generated for various scenarios of fluidic model by variation of involved pertinent parameters via Galerkin weighted residual method (GWRM). In order to predict the (MLP) values, a multi-layer perceptron (MLP) artificial neural network (ANN) has been developed. There are 10 neurons in hidden layer of feed forward (FF) back propagation (BP) network model. The predictive performance of ANN model has been analyzed by comparing the results obtained from the ANN model using Levenberg-Marquard algorithm as the training algorithm with the target values. When the obtained Mean Square Error (MSE), Coefficient of Determination (R) and error rate values have been analyzed, it has been concluded that the ANN model can predict SFC and NN values with high accuracy. According to the findings of current analysis, ANN approach is accurate, effective and conveniently applicable for simulating the slip flow of Williamson fluid towards the stretching plate with heat generation/absorption. The obtained results showed that ANNs are an ideal tool that can be used to predict Skin Friction Coefficients and Nusselt Number values.

Abstract The current research explores incremental effect of thermal radiation on heat transfer improvement corresponds to Darcy–Forchheimer (DF) flow of carbon nanotubes along a stretched rotating surface using RSM. Casson carbon nanotubes’ constructed model in boundary layer flow is being investigated with implications of both single-walled CNTs and multi-walled CNTs. Water and Ethylene glycol are considered a basic fluid. The heat transfer rate is scrutinized via convective condition. Outcomes are observed and evaluated for both SWCNTs and MWCNTs. The Runge–Kutta Fehlberg technique of shooting is utilized to numerically solve transformed nonlinear ordinary differential system. The output parameters of interest are presumed to depend on governing input variables. In addition, sensitivity study is incorporated. It is noted that sensitivity of SFC via SWCNT-Water becomes higher by increasing values of permeability number. Additionaly, sensitivity of SFC via SWCNT-water towards the permeability number is higher than the solid volume fraction for medium and higher permeability levels. It is also noted that sensitivity of SFC (SWCNT-Ethylene-glycol) towards volume fraction is higher for increasing permeability as well as inertia coefficient. Additionally, the sensitivity of LNN towards the Solid volume fraction is higher than the radiation and Biot number for all levels of Biot number. The findings will provide initial direction for future device manufacturing.

The aim of this study is to analyze the number of deaths due to COVID-19 for Europe and China. For this purpose, we proposed a novel three parametric model named as Exponentiatedtransformation of Gumbel Type-II (ETGT-II) for modeling the two data sets of death cases due to COVID-19. Specific statistical attributes are derived and analyzed along with moments and associated measures, moments generating functions, uncertainty measures, complete/incomplete moments, survival function, quantile function and hazard function, etc. Additionally, model parameters are estimated by utilizing maximum likelihood method and Bayesian paradigm. To examine efficiency of the ETGT-II model a simulation analysis is performed. Finally, using the data sets of death cases of COVID-19 of Europe and China to show adaptability of suggested model. The results reveal that it may fit better than other well-known models.

Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was −0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.

In recent years, statisticians have grown increasingly engaged in research of mixture models, particularly in the previous decade, without adequate consideration of challenge of estimating the parameters of mixture models from a frequentist perspective. Except for maximum likelihood estimation, this study addresses this vacuum by discussing the two other classical methods of estimation for mixture model. We commence by briefly describing the three frequentist approaches, namely maximum likelihood, ordinary, and weighted least squares, and then comparing them through extensive numerical simulations. The model’s applicability is illustrated by its application to simulated and real-world data, which yields promising results in terms of enhanced estimation.

Statistical models perform an essential role in data analysis, and statisticians are constantly looking for novel or pretty recent statistical models to fit data sets across a broad variety of fields. In this study, we used modified Kies generalized transformation and the new power function to suggest an unique statistical model. We present and discuss a linear illustration of the density function. Theoretically, quantile function, characteristic function, stochastic ordering, mean, and moments are just a few of the structure properties we discuss. By defining an ideal statistical distribution for assessing the COVID-19 mortality rate, an attempt is performed to determine the model of COVID-19 spread in different nations like the United Kingdom and Italy. In some countries, the novel distribution have been shown to be more appropriate than existing competing models when fitted to COVID-19.

In a suspension of tangent hyperbolic bionanofluid keeping both nanoparticles and motile microorganisms, the thermobioconvective boundary layer flow was studied through an exponentially stretching surface utilizing response surface methodology (RSM). The constructed model of a tangent hyperbolic nanofluid in boundary layer flow is studied with implications of thermophoresis and Brownian motion. Condition of zero normal flux of nanomaterials is added at the surface to scatter the nanomaterials from the plate surface. The rate of heat transfer is analyzed using convective boundary condition. Numerical shooting strategy with Runge-Kutta scheme is to follow intently behind the similarity transformation to solve the system of governing equations. It is assumed that the output variables of interest are dependent on the governing input parameters. The sensitivity analysis is additionally introduced. It is discovered that the sensitivity of local Nusselt number increments by expanding Lewis and thermophoresis number while the highest non-dimensional Nusselt number appears close to the significant level for the thermophoresis and low level for the Brownian motion variable. Additionally, it is demonstrated that the average maximum mean thickness of motile microorganism appears at the highest level of Brownian motion and thermophoresis number and thermophoresis and Lewis numbers. The results would provide initial guidance for potential manufacture of devices.

This paper presents the Bayesian inference of the 3-component Kumaraswamy mixture distribution. The paper formulates the 3-component model, provides Bayesian estimates and their respective posterior risks assuming non-informative prior under type-I censoring. Since the Bayes estimates are not in closed form, the paper provides a comparative analysis of the estimates and their respective risks derived under quadrature method and importance sampling using different loss functions, assuming different sample sizes, test termination times and mixture probabilities. Finally, the proposed 3 component mixture model is then applied to the real life data to assess its validity. The convergence of the estimates under importance sampling is rapidly observed as compared to quadrature methods.

Paired comparison (PC) scheme is very helpful in ranking objects. In this paper, a new paired comparison model is developed using the Weibull distribution and analyzed in Bayesian framework using informative prior under different loss functions. Summary of posterior estimates is evaluated to assess the ranking of the social media applications, i.e., Facebook, Whatsapp, Instagram, Skype and Twitter. The ranking presented by the model is justifiable according to the prevalence of the usage of the social media applications. The developed model is found to be plausible by the goodness of fit criteria.

Statistical models play an important role in data analysis, and statisticians are constantly looking for new or relatively new statistical models to fit data sets across a wide range of fields. In this study, we used a new alpha power transformation and the Gumbel Type -II distribution to suggest an unique statistical model. The study contains a simulation analysis to determine the parameters’ efficiency. Two real-life data sets were utilized to demonstrate the use of novel alpha power Gumbel Type II (NAPGT-II) distribution. NAPGT-II distribution yields a better fit than Weibull, new alpha power exponential, exponentiated Gumbel Type-II, Gumbel Type-II and exponentiated generalized Gumbel Type-II distribution, as evidenced by the data.

This article presents an innovative model called Additive Trinomial Fré chet (ATF) distribution using six parameters. The indicated model is worthy of modeling survival data with a non-monotonic hazard rate. The statistical characteristics of ATF model such as probability generating function, Renyi, Shannon, Tsallis and Mathai–Houbold entropy, quantile function, order statistics, maximum likelihood estimation, factorial and characteristic function, moment generating function, Stress-Strength analysis are thoroughly discussed. The effectiveness of suggested model is demonstrated by the use of a data set from real life. The suggested model has demonstrated better performance and fits the data used superior than other significant counterparts.

Statistical models are vital in data analysis, and researchers are always on the search for potential or the latest statistical models to fit data sets in a variety of domains. To create an improved statistical model, we used a T-X transformation and the Gumbel Type-II model in this investigation. The research examined a simulation evaluation to assess the efficacy of the parameters. To show the application of the T-X approach for producing new distributions titled the new and improved Gumbel Type-II (NIGT-II) distribution, two actual data sets were used. The data sets reveal that the NIGT-II distribution sounds nicer than the Gumbel Type-II distribution.

Recently, the use of mixed models for analyzing real data sets with infinite domains has gained favor. However, only a specific type of mixture model using mostly maximum likelihood estimation technique has been exercised in the literature, and fitting the mixture models for bounded data (between zero and one) has been neglected. In statistical mechanics, unit distributions are widely utilized to explain practical numeric values ranging between zero and one. We presented a classical examination for the trade share data set using a mixture of two log-Bilal distributions (MLBDs). We examine the features and statistical estimation of the MLBD in connection with three techniques. The sensitivity of the presented estimators with respect to model parameters, weighting proportions, sample size, and different evaluation methodologies has also been discussed. A simulation investigation is also used to endorse the estimation results. The findings on maximum likelihood estimation were more persuasive than those of existing mixture models. The flexibility and importance of the proposed distribution are illustrated by means of real datasets.

The reliability of software has a tremendous influence on the reliability of systems. Software dependability models are frequently utilized to statistically analyze the reliability of software. Numerous reliability models are based on the nonhomogeneous Poisson method (NHPP). In this respect, in the current study, a novel NHPP model established on the basis of the new power function distribution is suggested. The mathematical formulas for its reliability measurements were found and are visually illustrated. The parameters of the suggested model are assessed utilizing the weighted nonlinear least-squares, maximum-likelihood, and nonlinear least-squares estimation techniques. The model is subsequently verified using a variety of reliability datasets. Four separate criteria were used to assess and compare the estimating techniques. Additionally, the effectiveness of the novel model is assessed and evaluated with two foundation models both objectively and subjectively. The implementation results reveal that our novel model performed well in the failure data that we examined.

In this study, we propose a new three-parameter lifetime model based on the type-I half-logistic G family and the unit-Gompertz model, which we named the half-logistic unit Gompertz type-I distribution. The key feature of such a novel model is that it adds a new tuning parameter to the unit-Gompertz model using the type-I half-logistic family in order to make the unit-Gompertz model more flexible. Diagrams and numerical results are used to look at the new model’s mathematical and statistical aspects. The efficiency of estimating the distribution parameters is measured using a variety of well-known classical methodologies, including Anderson–Darling, maximum likelihood, least squares, weighted least squares, right tail Anderson–Darling, and Cramer–von Mises estimation. Finally, using the maximum likelihood estimation method, the flexibility and ability of the proposed model are illustrated by means of re-analyzing two real datasets, and comparisons are provided with the fit accomplished by the unit-Gompertz, Kumaraswamy, unit-Weibull, and Kumaraswamy beta distributions for illustrative purposes.

The Shanker distribution, a one-parameter lifetime distribution with an increasing hazard rate function, is recommended by Shanker for modelling lifespan data. In this study, we examine the theoretical and practical implications of 2-component mixture of Shanker model (2-CMSM). A significant feature of proposed model’s hazard rate function is that it has rising, decreasing, and upside-down bathtub forms. We investigate the statistical characteristics of a mixed model, such as the probability-generating function, the factorial-moment-generating function, cumulants, the characteristic function, the Mills ratio, the mean residual life, and the mean time to failure. There is a graphic representation of density, mean, hazard rate functions, coefficient of variation, skewness, and kurtosis. Our final approach is to estimate the parameters of the mixture model using appropriate approaches such as maximum likelihood, least squares, and weighted least squares. Using a simulation analysis, we examined how the estimates behaved graphically. The simulation results demonstrated that, in the majority of cases, the maximum likelihood estimates have the smallest mean square errors among all other estimates. Finally, we observed that when the sample size rises, the precision measures decrease for all of the estimation techniques, indicating that all of the estimation approaches are consistent. Through two real data analyses, the suggested model’s validity and adaptability are contrasted with those of other models, including the mixture of the exponential distributions and the Lindley distributions .

The purpose of this paper is to identify an effective statistical distribution for examining COVID-19 mortality rates in Canada and Netherlands in order to model the distribution of COVID-19. The modified Kies Frechet (MKIF) model is an advanced three parameter lifetime distribution that was developed by incorporating the Frechet and modified Kies families. In particular with respect to current distributions, the latest one has very versatile probability functions: increasing, decreasing, and inverted U shapes are observed for the hazard rate functions, indicating that the capability of adaptability of the model. A straight forward linear representation of PDF, moment generating functions, Probability weighted moments and hazard rate functions are among the enticing features of this novel distribution. We used three different estimation methodologies to estimate the pertinent parameters of MKIF model like least squares estimators (LSEs), maximum likelihood estimators (MLEs) and weighted least squares estimators (WLSEs). The efficiency of these estimators is assessed using a thorough Monte Carlo simulation analysis. We evaluated the newest model for a variety of data sets to examine how effectively it handled data modeling. The real implementation demonstrates that the proposed model outperforms competing models and can be selected as a superior model for developing a statistical model for COVID-19 data and other similar data sets.

In this paper, we propose a two-component mixture of Akash model (TC-MAM). The behavior of TC-MAM distribution has been presented graphically. Moment-based measures, including skewness, index of dispersion, kurtosis, and coefficient of variation, have been determined and hazard rate functions are presented graphically. The probability generating function, Mills ratio, characteristic function, cumulants, mean time to failure, and factorial moment generating function are all statistical aspects of the mixed model that we explore. Furthermore, we figure out the relevant parameters of the mixture model using the most suitable methods, such as least square, weighted least square, and maximum likelihood mechanisms. Findings of simulation experiments to examine behavior of these estimates are graphically presented. Finally, a set of data taken from the real world is examined in order to demonstrate the new model’s practical perspectives. All of the metrics evaluated favor the new model and the superiority of proposed distribution over mixture of Lindley, Shanker, and exponential distributions.

Nanotechnology is a fundamental component of modern technology. Researchers have concentrated their efforts in recent years on inventing various algorithms to increase heat transmission rates. Using nanoparticles in host fluids to dramatically improve the thermal properties of ordinary fluids is one way to address this problem. The article deals with the bio-convective Walter’s B nanofluid with thermophoresis and Brownian diffusion through a cylindrical disk under artificial neural networks (ANNs). In addition, the thermal conductivity, radiation, and motile density of microorganisms are taken into consideration. The Buongiorno model is utilized to investigate the properties of nanofluids in motile microorganisms. By using appropriate similarity variables, a dimensionless system of a differential system is attained. The non-linear simplified system of equations has been numerically calculated via the Runge–Kutta fourth-order shooting process. The consequences of flow parameters on the velocity field, temperature distribution, species volumetric concentration, and microorganism fields are all addressed. Two distinct artificial neural network models were produced using numerical data, and their prediction performance was thoroughly examined. It is noted that according to the error histograms, the ANN model’s training phase has very little error. Furthermore, mean square error values calculated for local Nusselt number, local Sherwood number, and local motile density number, parameters were obtained as 3.58×10−3, 1.24×10−3, and 3.55×10−5, respectively. Both artificial neural network models can predict with high accuracy, according to the findings of the calculated performance parameters.

Current research is focused on the utilization of a numerical solution of Casson cobalt ferrite nanofluid flow by taking two forms of base fluid. This investigation includes the gradual influence of nonlinear thermal radiation on the improvement of heat transfer related to the flow of nanofluids over a stretched rotating surface by the Darcy–Forchheimer law. The model constructed by a Casson nanoliquid in the boundary layer’s flow is studied for its symmetric behaviour, including cobalt ferrite nanomaterials. Two base liquids named as ethylene glycol and water are considered. The rate of heat transport is examined by considering Newtonian heating conditions. By utilizing similarity transformations, a partial differential system that governs the said model has been transformed into a highly nonlinear ordinary differential system, and numerical outcomes are obtained by implementing the RK4 via shooting methodologies. All obtained results, including local skin friction coefficients and local Nusselt number, are defined and discussed in the paper. The study’s findings ensure that the Casson cobalt ferrite nanofluid flowing towards a stretching plate has a unique solution: A variation of the solid volume fraction corresponds to the decrease in various values of the Casson nanofluid parameter for both type of nanofluid. Furthermore, a similar behaviour is noted for various values of the solid volume fraction, which corresponds to various values of the inertia coefficient parameter. Moreover, for the highest values of the solid volume fraction and all values of R1 and Ni taken into account, the rate of heat transfer upsurges. The data from the local skin friction coefficient (LSFC) and local Nusselt number (LNN) have been analysed using various statistical distributions, and it has been determined that both datasets generally fit the exponentiated Weibull distribution for various values of considered parameters. The findings would serve as a starting point for the manufacture of devices.

In this article, we develop a generator to suggest a generalization of the Gumbel type-II model known as generalized log-exponential transformation of Gumbel Type-II (GLET-GTII), which extends a more flexible model for modeling life data. Owing to basic transformation containing an extra parameter, every existing lifetime model can be made more flexible with suggested development. Some specific statistical attributes of the GLET-GTII are investigated, such as quantiles, uncertainty measures, survival function, moments, reliability, and hazard function etc. We describe two methods of parametric estimations of GLET-GTII discussed by using maximum likelihood estimators and Bayesian paradigm. The Monte Carlo simulation analysis shows that estimators are consistent. Two real life implementations are performed to scrutinize the suitability of our current strategy. These real life data is related to Infectious diseases (COVID-19). These applications identify that by using the current approach, our proposed model outperforms than other well known existing models available in the literature.

This study deals with the cumulative charting technique based on a simple and a mixture of Rayleigh models. The respective charting schemes are referred as the SRCQC-chart and the MRCQC-chart. These are stimulated from existing statistical control charts in this direction i.e. the cumulative quantity control (CQC) chart, based on exponential and Weibull models, and the cumulative count control (CCC) chart, based on the simple geometric model. Another motivation for this study is the mixture cumulative count control (MCCC) chart based on the two component geometric model. The use of mixture cumulative quantity is an attractive approach for process monitoring. The design structure of the proposed control chart is derived by using the cumulative distribution function of simple, and two components of mixture distribution(s). We observed that the proposed charting structure is efficient in detecting the changes in process parameters. The application of the proposed scheme is illustrated using a real dataset.