Heat transference in fluid mechanism has a deep influence in real-life applications like hot-mix paving, recovery of energy, concrete heating, heat spacing, refineries, distillation, autoclaves, reactors, air conditioning, and so forth. In this attempt, findings related to energy exchange with features of infinite shear rate viscosity model of Carreau nanofluid by placing inclined magnetic dipole over the wedge are made. The main role in the transportation of heat is exercised by incorporating facts of radiation, nonuniform heat sink source, Brownian motion, thermophoresis, and chemical reaction. The mathematical system of the infinite shear rate viscosity model of Carreau nanofluid gives a system of partial differential equations and furthermore, these are moved into ordinary differential equations. A numerical procedure is applied via shooting/bvp4c to obtain numerical results. Inclined magnetic dipole gives a lower velocity of Carreau nanofluid. Due to the relaxation time factor velocity of Carreau fluid gets down. A* causes to generate the heat internally, so due to this, temperature increases

No abstract

The current investigation explains the chemical reaction and bioconvection process for an inclined magnetized Cross nanofluid over an inclined cylinder using a spectral relaxation approach. Additionally, the facts concerning swimming gyrotactic microorganisms, non-uniform thermal conductivity, and variable decrease or increase in heat sources are taken together. Each profile is checked for inclined and orthogonal magnetic impact. Appropriate transformations made for conversion of nonlinear PDEs into systems of ODEs. For obtaining numerical results, a spectral relaxation approach is utilized, and graphs are plotted with each physical parameter attached. It is well established that the temperature field intensifies owing to an amplification of thermal conduction and Brownian diffusivity phenomena. The heat transfer rate amplifies owing to a magnification in magnetic parameter and thermal conductivity, but the velocity field diminishes as a result of magnification in the Weissenberg number and power law index. Amplification in the reaction rate constant parameter diminishes the concentration field. Activation energy is the key factor responsible for magnification in the concentration field. Furthermore, smooth agreement is found during comparison with the existing literature. Statistical analysis is also conducted for physical quantities.

<abstract> <p>This work aims to provide the numerical performances of the computer epidemic virus model with the time delay effects using the stochastic Levenberg-Marquardt backpropagation neural networks (LMBP-NNs). The computer epidemic virus model with the time delay effects is categorized into four dynamics, the uninfected <italic>S</italic>(<italic>x</italic>) computers, the latently infected <italic>L</italic>(<italic>x</italic>) computers, the breaking-out <italic>B</italic>(<italic>x</italic>) computers, and the antivirus PC's aptitude <italic>R</italic>(<italic>x</italic>). The LMBP-NNs approach has been used to numerically simulate three cases of the computer virus epidemic system with delay effects. The stochastic framework for the computer epidemic virus system with the time delay effects is provided using the selection of data with 11%, 13%, and 76% for testing, training, and verification together with 15 neurons. The proposed and data-based Adam technique is overlapped to execute the LMBP-NNs method's exactness. The constancy, authentication, precision, and capability of the LMBP-NNs scheme are perceived with the analysis of the state transition measures, regression actions, correlation performances, error histograms, and mean square error measures.</p> </abstract>

ECG (electrocardiogram) identifies and traces targets and is commonly employed in cardiac disease detection. It is necessary for monitoring precise target trajectories. Estimations of ECG are nonlinear as the parameters TDEs (time delays) and Doppler shifts are computed on receipt of echoes where EKFs (extended Kalman filters) and electrocardiogram have not been examined for computations. ECG, certain times, results in poor accuracies and low SNRs (signal-to-noise ratios), especially while encountering complicated environments. This work proposes to track online filter performances while using optimization techniques to enhance outcomes with the removal of noise in the signal. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using IMCEHOs (Improved Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system nonlinearity where multi-iterative function (Optimized Iterative UKFs) predicts a target’s unknown parameters. To obtain optimal solutions theoretically, multi-iterative function takes less iteration, resulting in shorter execution times. The proposed multi-iterative function provides numerical approximations, which are derivative-free implementations. Signals are updated in the cloud environment; the updates are received by the patients from home. The simulation evaluation results with estimators show better performances in terms of reduced NMSEs (normalized mean square errors), RMSEs (root mean squared errors), SNRs, variances, and better accuracies than current approaches. Machine learning algorithms have been used to predict the stages of heart disease, which is updated to the patient in the cloud environment. The proposed work has a 91.0% accuracy rate with an error rate of 0.05% by reducing noise levels.