Explore the vast range of titles available.

The primary goal of this research is to present a novel computational intelligence approach of the AI-based Levenberg–Marquardt scheme under the influence of backpropagated neural network (LMS-BPNN) for optimizing MHD ternary hybrid nanofluid using Casson fluid over a porous shrinking sheet in the existence of thermal radiation (Rd) effects. The governing partial differential equations (PDEs) showing the Casson ternary hybrid nanofluid are converted into a system of ordinary differential equations (ODEs) with suitable transformations. The numerical data is constructed as a reference with bvp4c (MATLAB built-in function used to solve a system of ODEs) by varying Casson fluid parameters (β), magnetic field (M), porosity (S), nanoparticle concentrations (ϕ1=ϕ2=ϕ3), and thermal radiation (Rd) effects across all LMS-BPNN scenarios. The numerical data-sheet is divided into 80% of training, 10% of testing, and 10% of validation for LMS-BPNN are used to analyze the estimated solution and its assessment with a numerical solution using bvp4c is discussed. The efficiency and consistency of LMS-BPNN are confirmed via mean squared error (MSE) based fitness curves, regression analysis, correlation index (R) and error histogram. The results show that velocity decreases as β grows, whereas velocity increase as M increases. The concentrations of nanoparticles and thermal radiations have increasing effects on θ0. To comprehend the dependability and correctness of the data gained from numerical simulations, error analysis is a key stage in every scientific inquiry. Error analysis is presented in terms of absolute error and it is noticed that the error between the numerical values and predicted values with AI is approximately 10−6. The error analysis reveals that the developed AI algorithm is consistent and reliable.

Most next-generation Li ion battery chemistries require a functioning lithium metal (Li) anode. However, its application in secondary batteries has been inhibited because of uncontrollable dendrite growth during cycling. Mechanical suppression of dendrite growth through solid polymer electrolytes (SPEs) or through robust separators has shown the most potential for alleviating this problem. Studies of the mechanical behavior of Li at any length scale and temperature are limited because of its extreme reactivity, which renders sample preparation, transfer, microstructure characterization, and mechanical testing extremely challenging. We conduct nanomechanical experiments in an in situ scanning electron microscope and show that micrometer-sized Li attains extremely high strengths of 105 MPa at room temperature and of 35 MPa at 90 °C. We demonstrate that single-crystalline Li exhibits a power-law size effect at the micrometer and submicrometer length scales, with the strengthening exponent of −0.68 at room temperature and of −1.00 at 90 °C. We also report the elastic and shear moduli as a function of crystallographic orientation gleaned from experiments and first-principles calculations, which show a high level of anisotropy up to the melting point, where the elastic and shear moduli vary by a factor of ∼4 between the stiffest and most compliant orientations. The emergence of such high strengths in small-scale Li and sensitivity of this metal’s stiffness to crystallographic orientation help explain why the existing methods of dendrite suppression have been mainly unsuccessful and have significant implications for practical design of future-generation batteries.

Dendrite-free electrodeposition of lithium metal is necessary for the adoption of high energy-density rechargeable lithium metal batteries. Here, we demonstrate a mechanism of using a liquid crystalline electrolyte to suppress dendrite growth with a lithium metal anode. A nematic liquid crystalline electrolyte modifies the kinetics of electrodeposition by introducing additional overpotential due to its bulk-distortion and anchoring free energy. By extending the phase-field model, we simulate the morphological evolution of the metal anode and explore the role of bulk-distortion and anchoring strengths on the electrodeposition process. We find that adsorption energy of liquid crystalline molecules on a lithium surface can be a good descriptor for the anchoring energy and obtain it using first-principles density functional theory calculations. Unlike other extrinsic mechanisms, we find that liquid crystals with high anchoring strengths can ensure smooth electrodeposition of lithium metal, thus paving the way for practical applications in rechargeable batteries based on metal anodes.

Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.

The influence of Brownian motion and thermophoresis on a fluid containing nanoparticles flowing over a stretchable cylinder is examined. The classical Navier-Stokes equations are considered in a porous frame. In addition, the Lorentz force is taken into account. The controlling coupled nonlinear partial differential equations are transformed into a system of first order ordinary differential equations by means of a similarity transformation. The resulting system of equations is solved by employing a shooting approach properly implemented in MATLAB. The evolution of the boundary layer and the growing velocity is shown graphically together with the related profiles of concentration and temperature. The magnetic field has a different influence (in terms of trends) on velocity and concentration.

The multiphase fluid flow under the influence of electro-magnetohydrodynamics (EHD) is investigated in this study. The base fluid contains hafnium particles. Two illustrative models namely fluid phase and particulate phase are considered for three different geometries having great importance in both industrial and mechanical usage. The impact of pertinent parameters from different aspects is illustrated graphically with requisite discussion keeping in view their physical aspects. The stream lines are also erected to highlight their physical importance regarding the flow patterns. In addition, the paper is terminated by making a comparison with the existing literature as a limiting case of considered problem to confirm the validations of achieved results and hence found in excellent agreement. This model can be used to design and engineer for nozzle or diffuser type of injectors in the latest models of automobiles to improve their performance and reduce the consumption of fuel.

In this article, an investigation has been performed to explore the two-dimensional boundary layer flow problem and heat transfer characteristic of ferromagnetic viscoelastic fluid flow over a stretching surface with a linear velocity under the impact of magnetic dipole and suction. The governing PDEs are converted into a system of nonlinear ODEs by applying appropriate similarity approach. The modelled equations are then solved numerically by utilizing efficient Runge–Kutta–Fehlberg procedure based on shooting algorithm. Influence of pertinent flow parameter involved, such as ferromagnetic interaction parameter, suction parameter, viscoelastic parameter, Prandtl number on dimensionless velocity, temperature, skin friction, and Nusselt inside the boundary layer, are portrayed graphically and discussed. The results show that pressure profile and skin friction coefficient increase with the variation of ferromagnetic interaction parameter and opposite behaviour is noted for local Nusselt number.

This research presents a novel conformable-Caputo fractional non-polynomial spline method for solving the time-fractional Korteweg-de Vries (KdV) equation. Emphasizing numerical analysis and algorithm development, the method offers enhanced precision and modeling capabilities. Evaluation via the Von Neumann method demonstrates unconditional stability within defined parameters. Comparative analysis, supported by contour and 2D/3D graphs, validates the method’s accuracy and efficiency against existing approaches. Quantitative assessment using L 2 and L ∞ error norms confirms its superiority. In conclusion, the study proposes a robust solution for the time-fractional KdV equation.

This study tackles the critical challenge of waste material recycling, which is worsened by the growing global population and the necessity for efficient recycling processes. To address this issue, we introduce a pioneering approach that leverages circular Pythagorean fuzzy set theory, a sophisticated extension of fuzzy and intuitionistic fuzzy information. By formulating Muirhead mean and dual Muirhead mean aggregation operators within this framework, we facilitate structured and intelligent decision-making for assessing waste recycling alternatives. Our methodology and algorithm for multi-criteria group decision-making problems are showcased through a practical example, highlighting the efficacy and dependability of our approach. This research makes a significant contribution to the development of more efficient waste recycling processes and informed decision-making. The proposed approach enables decision-makers to evaluate waste recycling alternatives more comprehensively and systematically, taking into account multiple criteria and stakeholder perspectives. The findings of this study have important implications for policymakers, waste management professionals, and stakeholders seeking to improve waste recycling practices and reduce environmental impacts. By providing a more effective and reliable decision-making framework, this research aims to support the development of sustainable waste management systems. A sensitivity analysis illustrates the effectiveness and reliability of the proposed work. Finally, we adopted the comparative study and highlighted the advantages of defined work.

This study aims to propose a central composite design (CCD) combined with response surface methodology (RSM) to create a statistical experimental design. A new parametric optimization of entropy generation is presented. The flow behavior of magnetohydrodynamic hybrid nanofluid (HNF) flow through two flat contracting expanding plates of channel alongside radiative heat transmission was considered. The lower fixed plate was externally heated whereas the upper porous plate was cooled by injecting a coolant fluid with a uniform velocity inside the channel. The resulting equations were solved by the Homotopic Analysis Method using MATHEMATICA 10 and Minitab 17.1. The design consists of several input factors, namely a magnetic field parameter (M), radiation parameter (N) and group parameter (Br/A1). To obtain the values of flow response parameters, numerical experiments were used. Variables, especially the entropy generation (Ne), were considered for each combination of design. The resulting RSM empirical model obtained a high coefficient of determination, reaching 99.97% for the entropy generation number (Ne). These values show an excellent fit of the model to the data.