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The goal of this research is to consider the thermal impact on varied convection flow in hybrid nanofluids with heat generation over a two-dimensional heated flat around a stretchable sheet. The flow is considered steady and incompressible while the stretchable sheet is assumed an impermeable. Two distinctive nano-level particles are considered, namely copper (Cu) and silver (Ag) with ethylene glycol base fluid. The boundary layer was generated on a stretchable sheet surface by mixed convection flow in hybrid nanofluids. Ideally, the sink and source are thermal reservoirs of internal thermal capacities. This means you can extract or reject heat from them without changing their temperature. To make a study of thermodynamic systems like heat engines and refrigerator systems, the governing equations were solved numerically with Keller-box methodology depending on the implicit finite-difference technique. Research findings were worked with the parameters of mixed convection, Prandtl number, nanoparticle volume fraction, through various non-dimensional parameters, and heat generation. Especially for thermal generation enhancement, the fluidity and thermal dispersal get elevated. Even though the flowing behavior and the thermal dispersal of hybridity fluids with the combinations of Cu and Ag nanoparticles were similar, their values are distinct, which reflect in graphical displays. The hybrid nanofluidity gets improved with the volume variation of nanoparticles if the ϕ\phi value is 0.01≤ϕ≤0.050.01\le \phi \le 0.05 and if the flow profile value decreases ϕh{\phi }_{{\rm{h}}}, where 0.01≤ϕh≤0.050.01\le {\phi }_{{\rm{h}}}\le 0.05 as the dispersal of temperature enhances when the nanoparticle nanofluid constraint is improved.

In this paper, the implementation of the Fractional Variational iteration method (FVIM) is used to approximate the generalized fractional perturbed Zakharove-Kuznetsov equation. The obtained results show that the suggested approach is very efficient for dealing with various forms of differential equations of fractional order. The numerical comparison for the attained resolution is made through the q-homotopy analysis transmute manner (q-HATM) which confirms the convergence of the devised method. The applicability of the method is observed by applying the proposed technique to two numerical examples. Furthermore, the solutions obtained by the FVIM are completely well-matched with the solutions presented in the literature. [ABSTRACT FROM AUTHOR]

This work aims to analyze the impacts of the magnetic field, activation of energy, thermal radiation, thermophoresis, and Brownian effects on the hybrid nanofluid (HNF) (Ag++silicon oil) flow past a porous spinning disk. The pressure loss due to porosity is constituted by the Darcy-Forchheimer relation. The modified Buongiorno model is considered for simulating the flow field into a mathematical form. The modeled problem is further simplified with the new group of dimensionless variables and further transformed into a first-order system of equations. The reduced system is further analyzed with the Levenberg-Marquardt algorithm using a trained artificial neural network (ANN) with a tolerance, step size of 0.001, and 1,000 epochs. The state variables under the impacts of the pertinent parameters are assessed with graphs and tables. It has been observed that when the magnetic parameter increases, the velocity gradient of mono and hybrid nanofluids (NFs) decreases. As the input of the Darcy-Forchheimer parameter increases, the velocity profiles decrease. The result shows that as the thermophoresis parameter increases, temperature and concentration increase as well. When the activation energy parameter increases, the concentration profile becomes higher. For a deep insight into the analysis of the problem, a statistical approach for data fitting in the form of regression lines and error histograms for NF and HNF is presented. The regression lines show that 100% of the data is used in curve fitting, while the error histograms depict the minimal zero error -7.1e6 for the increasing values of Nt. Furthermore, the mean square error and performance validation for each varying parameter are presented. For validation, the present results are compared with the available literature in the form of a table, where the current results show great agreement with the existing one. [ABSTRACT FROM AUTHOR]

Multidrug resistance (MDR) remains a formidable challenge in cancer treatment, necessitating innovative strategies to enhance therapeutic outcomes. This review explores the potential of a synergistic gold nanorod (GNR) therapy (SGNRT) utilizing GNRs as multifunctional platforms for co‐delivering chemotherapy and thermal therapies. The rational design of SGNRT systems enables targeted payload delivery, circumvention of Adenosine triphosphate (ATP)‐binding cassette drug efflux transporters, and hyperthermia‐induced chemo sensitization. In vitro studies demonstrate the synergistic impact of SGNRT in overcoming MDR, emphasizing its potential for enhanced antitumor efficacy. However, further in vivo investigations are essential to assess the clinical viability of this nanoparticle (NP)‐directed approach against advanced multidrug‐resistant malignancies. The integration of SGNRT holds promise for advancing precision cancer therapies and addressing the intricate challenges of drug resistance in clinical settings. [ABSTRACT FROM AUTHOR]

Academics encounter a challenge regulating data-driven unpredictability in numerous complicated decision scenarios. Regulating the cyclical nature of appraisal attributes, determining lower and higher limitations, granting multi-parametric values as a means of assessing argumentation, and modeling uncertainty are a few examples of these problems. It requires the incorporation of complex plane settings, interval-valued intuitionistic fuzzy settings, and hypersoft settings. Inspired by these kinds of scenarios, the goal of this research was to articulate a new theoretical framework, the interval-valued complex intuitionistic fuzzy hypersoft set (Γ-set), which can handle these kinds of problems as a whole under the umbrella of a single framework. First, the concepts of Γ-set, as well as its set operations and aggregations, such as decision matrix, cardinal matrix, aggregate matrix, and cardinality set, were examined. The second phase offers an appealing algorithm that consists of nine steps that go from taking into account necessary set construction to making the best choice. A prototype case study analyzing eighteen evaluation qualities and thirty-four sub-attributes for determining an optimal cooling system (CSYS) for a factory validates the provided algorithm. Informative comparison analysis and preferred study features were provided as essential components of research to assist academics in making significant advances regarding their field and gradually, but thoroughly, advancing their specialization. [ABSTRACT FROM AUTHOR]

The fluid flow across spinning discs has several applications in different fields of engineering such as flywheels, gas turbine engines, brakes, and gears. The significance of heat source, Hall current, and magnetic field on the nanofluid flow between two spinning disks is studied in the present analysis. The upper disk spins as well as rotates, which generates the 3D flow. The evaluation and modeling of the mass density, flow motion, and energy transfer are performed using a system of partial differential equations (PDEs). The computational technique parametric continuation method (PCM) is employed to solve the modeled equations. The outcomes are relatively compared to the published literature for validity purposes. It has been noticed that during the downward motion of the upper disc, the effect of the magnetic field declines, while the influence of the hall current improves the velocity field. The temperature field rises with the ascending motion of the upper disk, whereas it shrinks with the downhill oscillation. Moreover, the mass diffusion ratio develops with the impact of hall current, while lessens with the influence of chemical reaction. [ABSTRACT FROM AUTHOR]

In magnetohydrodynamics (MHD) flowing and mass transfer, 2-D unsteady non-Newtonian liquid flow across the stretched surface with a surface temperature had been examined numerically. Casson fluid model was utilized for the full description of the non-Newtonian fluid. Similarity transformation had been considered for transforming the dimensionless heat and fluid flow basic partial differential equations into simple ordinary equations. After that, the bvp4c MATLAB solver utilizes numerical methods to resolve the altered equations. We study and discuss in depth the flowing and heating attributes under various parameters, including the magnetic, Casson, and unsteadiness parameters, Schmidt number, and Prandtl number. It has been proven numerically that the velocity of the fluid first reduces when temperature and unsteadiness parameters increase, which significantly negatively impacts concentration. The velocity field is suppressed when the Casson parameter's values are increased. But when with the increment of the Casson variable, then, the concentration and temperature go up. The velocity field is suppressed as the magnetic parameter's values increase. But with the rise in the magnetic parameter, there will be an improvement in both the temperature and concentration. [ABSTRACT FROM AUTHOR]

Supplier selection as a multiattribute decision-making (MADM) problem has various inherent uncertainties due to a number of symmetrical variables. In order to handle such information-based uncertainties, rational models like intuitionistic fuzzy sets have already been introduced in the literature. However, a picture fuzzy set (PiFS) with four dimensions of positive, neutral, negative, and rejection is better at capturing and interpreting such kinds of ambiguous information. Additionally, fuzzy parameterization (FPara) is helpful for evaluating the degree of uncertainty in the parameters. This study aims to develop a fuzzy parameterized picture fuzzy soft set (FpPiFSS) by integrating the ideas of PiFS and FPara. This integration is more adaptable and practical since it helps decision makers manage approximation depending on their objectivity and parameterization uncertainty. With the assistance of instructive examples, some of the set-theoretic operations are examined. A decision support framework is constructed using matrix manipulation, preferential weighting, fuzzy parameterized grades based on Pythagorean means, and the approximations of decision makers. This framework proposes a reliable algorithm to evaluate four timber suppliers (initially scrutinized by perusal process) based on eight categorical parameters for real estate projects. In order to accomplish suppliers evaluation, crucial validation outcomes are taken into account, including delivery level, purchase cost, capacity, product quality, lead time, green degree, location, and flexibility. To assess the advantages, dependability, and flexibility of the recommended strategy, comparisons in terms of computation and structure are provided. Consequently, the results are found to be reliable, analog, and consistent despite the use of fuzzy parameterization and picture fuzzy setting. [ABSTRACT FROM AUTHOR]

In this work, an artificial neural network based on the Cuckoo search algorithm (CS-ANN) is implemented for squeezing flow problems. Three problems are considered: the squeezing flow, the MHD squeezing flow, and the flow of the third-grade fluid past a moving belt. First, the approximation for the said nonlinear differential equations is explained and the proposed problems are transformed into the L 2 norms of minimization problems. Then, a well-known Cuckoo search algorithm is used to minimize the norms of each problem to get the best set of weights for artificial neural networks. The outcome of the proposed method is displayed through graphs. Two cases for each problem are discussed consisting of the solution, error, weights, and fitness function, respectively. The numerical results for the state variables are displayed in Tables. The error analysis in each case proves the accuracy of our implemented technique. The results are validated through graphs by comparing CS-ANN results with the gradient descent method. [ABSTRACT FROM AUTHOR]