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In a stagnation point flow, the rate at which heat transfers in fluid containing nanoparticles across a sheet that is stretchable on a surface having pores has been investigated in this research. Magnetohydrodynamic viscous nanofluid flow is considered that is subjected to Brownian movements and the thermophoresis effect. By utilizing a numerical technique, the characteristics of heat transmission in nanofluids are investigated. The model is based on momentum, energy, and concentration equations. To explain the flow model’s physical significance, zero mass flux condition has been employed at the surface. Nonlinear partial differential equations are transformed into a collection of linked ordinary differential equations via similarity transformations. Convergent implications of nonlinear systems are produced by MATLAB software’s built-in bvp4c algorithm. To indicate the physical importance, a thorough examination of relevant characteristics, such as heat sink/source, porosity, and magnetic parameter is conducted. We have observed the behavior of profiles by fixing the numerical values of the involving parameters as 0.1 ≤ λ ≤ 2.0, 0.1 ≤ Nr ≤ 3.0, 0.1 ≤ R ≤ 0.4, 0.1 ≤ M ≤ 0.4, 0.1 ≤ Rb ≤ 1.5, and 0.1 ≤ Nb ≤ 0.7. The temperature rises yet the rate at which heat transfers at the surface declines due to the increased far-field velocity. The greater nanoparticles concentration at the far field relative to the surface is related to the zero mass flux condition.

Significance: The CuO, Al2O3, and TiO2 nanoparticles find extensive applications in advanced chemical reaction-based thermal transport and quadratic convective nanofluids due to their exceptional thermal properties and chemical stability. Increased thermal conductivity of these nanoparticles used to enhance heat transfer in various chemical reactors, resistance to chemical degradation and photocatalytic reactors and solar energy applications. Motive: This study brings the investigation about quadratic convection-based thermal transport to infinite shear rate for magnetized ternary radiative cross nanofluid with homogeneous-heterogeneous chemical reactions. Water is taken as base fluid and three nanoparticles are Copper oxide (CuO), aluminium oxide (Al2O3), and titanium dioxide (TiO2). Heat transport analysis is made through quadratic convection, magnetic field and thermal radiation. Concentration of nanofluid is scrutinized though homogeneous-heterogeneous chemical reactions. Method: ology: Physical problem with assumptions generates the system of partial differential equations (PDEs) and these PDEs are transformed into ordinary differential equations (ODEs) through similarity variables. Furthermore, a unique combination of Bvp4c and Levenberg Marquardt neural network (LM-NN) schemes is utilized to fetch the numerical solutions. Bvp4c is utilized to solve the governing equations, while LM-NN serves to enhance predictive capabilities and capture intricate nonlinear relationships. Findings: Magnetic environment, chemical process, radiations effects and volumetric fraction of nanoparticles make better heat transfer efficiency and control.

Abstract This research presents a new adaptive exponentially weighted moving average control chart, known as the coefficient of variation (CV) EWMA statistic to study the relative process variability. The production process CV monitoring is a long-term process observation with an unstable mean. Therefore, a new modified adaptive exponentially weighted moving average (AAEWMA) CV monitoring chart using a novel function hereafter referred to as the "AAEWMA CV" monitoring control chart. the novelty of the suggested AAEWMA CV chart statistic is to identify the infrequent process CV changes. A continuous function is suggested to be used to adapt the plotting statistic smoothing constant value as per the process estimated shift size that arises in the CV parametric values. The Monte Carlo simulation method is used to compute the run-length values, which are used to analyze efficiency. The existing AEWMA CV chart is less effective than the proposed AAEWMA CV chart. An industrial data example is used to examine the strength of the proposed AAEWMA CV chart and to clarify the implementation specifics which is provided in the example section. The results strongly recommend the implementation of the proposed AAEWMA CV control chart.

The significant impact of nanotechnology has turned the ordinary into the excellent in the ever-changing environment of science and technology. Recent times have seen a dramatic transformation as advances in science and technology continue to push us into the domain of nanoscale innovation. Fluid flow over a range of geometries is involved in the physical processes of heat and mass transfer subject to the constraints. These phase change systems are also converted into the latest technology by improving the thermal conductivity of the fluids through the mixing of nanoparticles in the ordinary base fluid. This approach makes things finer and quicker from the perspective of efficiency and structure. It has many applications in various domains especially in nano-medicines, chemotherapy, microprocessors, refrigeration, and biotechnology. In this work, nanofluid flow through a wavy cylinder is considered in the existence of thermal radiation, activation energy, and motile microorganisms. The physical model is computationally solved by developing a system of PDE's (partial differential equations) and then transformed into ODE's (ordinary differential equations) by the smooth implementation of similarity variables. The resultant ODE's numerically treated by the bvp4c built package of MATLAB and get the required results. These results are discussed and graphically visualized in the analysis section. For the validation of results, the statistical approach is implemented on the acquired results and shows the fitted model, contour plots, surface plots, residual plots, and streamlines of the involved parameters and their impacts on the model. The impact of involving physical quantities on flow velocity, thermal, concentration, and microorganism's density profiles also discussed. From the results, it is noted that the velocity profile increases by increasing the counts of mixed convection. Thermal distribution enhanced due to boosting the values of thermophoresis and Brownian motion. The concentration of nanoparticles increased by increasing the magnetic field strength. The larger values of peclet number minimize the density of microorganisms. Skin friction coefficient is increased by around 28% and mass transport going to be increased by 36% due to the existence of microorganisms. The analysis of variance shows that our model is significant and the fitted summary also shows the fitness of model.

The average lifespan of particles, a crucial parameter in nuclear physics, is essential for identification purposes. Modern particle detectors excel at recognizing individual radioactive nuclei arrivals and their subsequent decay events. However, challenges arise when matching arrivals with departures, especially when departures are only partially observed. One inefficient approach involves conducting experiments with very low arrival rates to facilitate matching. The kiloelectron-volt E(keV) emission is obtained during this radio active process. This study focuses on the meticulous surveillance of the average keV emission from partially observed events within the domain of nuclear physics. To accomplish this, the methodology employs the statistical approach known as Distance Weighted Mean (DWM), integrated with the application of censored control charts. The utilization of censored control charts allows for the effective management of incomplete data, enabling researchers to make informed decisions despite potential limitations in observation. We propose a DWM based exponentially weighted moving average-cumulative sum (DWM-EC) control chart for monitoring kiloelectron-volt E(keV) data. The proposed charts is developed for Weibull lifetimes under type-I censoring. For the construction of an efficient control charting structure, we employed the conditional median (CM) methods. The goal is to find changes in the mean of Weibull lifetimes with censored data with known and estimated parameter conditions. The performance of the proposed DWM-EC chart is evaluated by the average run length (ARL). Besides a simulation study, a real-life data set on E(keV) related to the alpha decays of 177 Lutetium isotope is also discussed.

Heat transport and energy storage continue to be major issues for manufacturers and scientists. So far, the notion of novel heat transfer fluids has been developed, namely nanofluids and hybrid nanofluids. This investigation examines thermal radiation effect on heat transfer in MHD flow using the hybrid nanofluid. It examines a hybrid nanofluid (SWCNT-Al2O3/H2H6O2–H2O and MWCNT-CuO/H2H6O2–H2O) with H2O water base fluid nature passing through a starching surface with thermal radiation, Biot number and melting phenomena. The suitable similarity transformations are used to transmute the main governing system of equation PDEs and the appropriate boundary conditions for computation with the help of well-reputed shooting technique. The graphical and numerical outcomes against dissimilar flow parameters are computed by the mathematical software MATLAB. The velocity distribution profile is decreased by growing values of the Rotation parameter and porosity parameter. The velocity profile is enhanced by increasing the values of Darcy forchheimer medium. The thermal distribution field is enhanced by rising estimations of the thermal radiation parameter and Biot number. Temperature of the fluid is increasing due to increase in thermal conductivity parameter while decreasing due to increase in Marangoni ratio parameter. The present model hybrid nanofluid of heat transmission is evaluated to see if it has higher thermal energy storage efficiency than standard nanofluids. As a result, these novel findings in heat transport might be advantageous in dealing with energy storage issues in the modern technological environment.

The removal of irrelevant and insignificant genes has always been a major step in microarray data analysis. The application of gene selection methods in biological datasets has greatly increased, supporting expert systems in cancer diagnostic capability with high classification accuracy. Penalized logistic regression (PLR) using the elastic net (EN) has been widely used in high-dimensional cancer classification in recent years to estimate the gene coefficients and perform gene selection simultaneously. However, the EN estimator does not satisfy the oracle properties. This paper proposes the PLR using the adaptive elastic net (AEN), abbreviated as PLRAEN, to address the inconsistency. Our method employs the ratio (BWR) as an initial weight inside the L1-norm of the EN model. Several experiments were performed on a simulation study for a different number of predictor variables, sample sizes, and correlation coefficients and also on three public gene expression datasets to evaluate the effectiveness. Experimental results demonstrate that the proposed method consistently outperforms two other contemporary penalized methods regarding classification accuracy and the number of selected genes. Therefore, we conclude that PLRAEN is a better method to implement gene selection in the high-dimensional cancer classification field.