Search

Nanofluidics have better thermal properties than regular fluids, which makes them useful for heat transfer applications. This research investigated the complex dynamics of confined magnetic forces that influence the rotation of nanostructures and vortex formation in a tri-hybrid nanofluid (Ag, Al2O3, TiO2) flow regime. The study shows that the magnetic field can change the flow and heat transfer of nanofluidic, depending on its direction and strength. The study also provides insights into the complex physics of nanofluid flow and heat transfer, which can help design devices that use nanofluids more efficiently for cooling electronics, harvesting solar energy, and generating power from fuel cells. We used a single-phase model to model the nanofluids while the governing partial differential equations were solved numerically. An alternating-direction implicit approach has been employed to analyze the impact of confined magnetic fields on the nanofluid flow and thermal properties. Unlike previous studies that assumed uniform magnetic fields, we introduced multiple confined magnetic fields in the form of horizontal and vertical strips. Using our custom MATLAB codes, we systematically examined various parameters, including the magnetic field strength, number of strips and their position, and nanoparticle volume fraction, to assess their effects on nanofluid flow and thermal characteristics. Our findings revealed that the confined Lorentz force induced the spinning of tri-hybrid nanoparticles, resulting in a complicated vortex structure within the flow regime. In the absence of a magnetic field, a single symmetric vortex can be seen in the flow field. However, the introduction of magnetic sources stretches this vortex until it splits into two smaller, weaker vortices in the lower cavity, rotating clockwise or counterclockwise. Furthermore, the magnetic field strength significantly reduces both skin friction and the Nusselt number, while Reynolds numbers mainly affect the Nusselt number.

Hybrid nanofluids can provide better physical strength, thermal conductivity, and mechanical resistance in many thermodynamic systems than pure nanofluids. To establish the novel results, using superior types of hybrid nanoparticles like graphene oxide (GO) and iron oxide (Fe3O4) is the main focus of recent work. This study investigates the innovative thermal and magnetic features of both pure nanofluid GO/engine oil (EO) and hybrid nanofluid GO–Fe3O4 /EO under the simultaneous effects of induced as well as applied magnetic field. The chemical reaction phenomenon together with activation energy has also been taken into account. A novel algorithm based on order reduction and finite difference discretization is developed in order to numerically treat the problem. The efficiency of the code is appraised by a numerical comparison which is found to be in a good correlation with the existing results. From the consequences of this study, it is deduced that the reduction in induced magnetic field and fluid’s velocity (in case of either pure or hybrid nanofluid) is associated with the enlarging values of magnetic Prandtl number and induced magnetic field parameter. Further, activation energy is responsible for enhancement in concentration. The hybrid nano-composition of GO–Fe3O4/EO can provide the thermal stability, prevent the corrosion and make the liquid to stay in high temperature.

In the current study, we numerically analyze the significance of motile microbes on the magnetohydrodynamic steady convective streams of tangent hyperbolic (TH) nanofluid flow across an elastic nonlinearly stretching surface of an irregular thickness. The consequences of an external magnetic field, thermal radiation, and thermal conductivity are also examined on the TH nanofluid. The governing system of equations (nonlinear set of partial differential equations) is transfigured into a system of ordinary differential equations (ODEs) by using the similarity variable conversions. Furthermore, the reduced form of nonlinear ODEs is numerically computed through the parametric continuation method (PCM) using MATLAB software. The relative evaluation is carried out to authenticate the numerical outcomes. It has been observed that the energy field accelerates with the Rayleigh number, Weissenberg number, and Brownian motion. The mass propagation ratio improves with the effect of activation energy and decreases with the influence of chemical reactions. Furthermore, the motile microbes’ profile declined with the outcome of the Peclet and Lewis numbers. The skin friction increases up to 7.3% with various magnetic values ranging from 0.5 to 1.5. However, the energy transfer rate declines to 5.92%. The thermal radiation boosts the energy propagation rate and flow velocity by up to 11.23 and 8.17%, respectively.

The focus of this article lies on the notion of the edge version of doubly resolving sets (EVDRSs) in circulant networks. EVDRSs refer to unique edge subsets that are necessary for identifying individual edges in a network and distinguishing them based on their edge distances to the elements of the EVDRS. The main objectives were to define the minimal size of EVDRSs for circulant networks Cn(1, 2) and to investigate their basic properties. The systematic research helped to achieve a new understanding of the existence, construction, and characterization of EVDRSs in circulant networks Cn(1, 2). It is established that the EVDRSs in the circulant network Cn(1, 2) are finite and are bounded by the order of the network. Among the numerous implications of these findings are those that refer to the design and optimization of distributed sensor networks, improving communication and network protocols, as well as tracking the spread of infectious diseases and epidemics over social networks. The application of the identified methodology helps improve the process of network optimization which contributes to the development of more effective and robust circulant-based structures. [ABSTRACT FROM AUTHOR]

Hybrid nanofluids are capable of being used as a carrier for delivering drugs to targeted areas in the body. For this purpose, the Casson hybrid nanofluids (HNFs) flow is suggested between the gap of two tubes. The study involves analyzing blood-based hybrid nanofluids (HNFs) that consist of silver (Ag) and titanium dioxide (TiO2) nanoparticles for applications of drug delivery. Titanium oxide nanoparticles, or TiO2 NPs, have strong photoactivity, low toxicity, and excellent biocompatibility, making them promising candidates for cancer treatment. The attractiveness of silver nanoparticles (AgNPs) for cancer therapy is due to their unique properties. Graphic representations are provided based on the simulations of non-dimensional velocity, temperature, and skin friction under physical parameter variations, for the applications of drug delivery are investigated and discussed. The control volume finite element method (CVFEM), is used to solve the problem. The new strategy of artificial neural networks (ANN) is also used to solve the transform equations. Nanoparticles increase drug stability and extend shelf life by protecting them from degradation or inactivation. [ABSTRACT FROM AUTHOR]

In the current study, a neutrosophic quadratic fractional programming (NQFP) problem is investigated using a new method. The NQFP problem is converted into the corresponding quadratic fractional programming (QFP) problem. The QFP is formulated by using the score function and hence it is converted to the linear programming problem (LPP) using the Taylor series, which can be solved by LPP techniques or software (e.g., Lingo). Finally, an example is given for illustration.

This article attempts to study cost minimizing multi-objective fractional solid transportation problem with fuzzy cost coefficients c˜ijkr{\tilde{c}}_{ijk}^{r}, fuzzy supply quantities a˜i{\tilde{a}}_{i}, fuzzy demands b˜j{\tilde{b}}_{j}, and/or fuzzy conveyances e˜k{\tilde{e}}_{k}. The fuzzy efficient concept is introduced in which the crisp efficient solution is extended. A necessary and sufficient condition for the solution is established. Fuzzy geometric programming approach is applied to solve the crisp problem by defining membership function so as to obtain the optimal compromise solution of a multi-objective two-stage problem. A linear membership function for the objective function is defined. The stability set of the first kind is defined and determined. A numerical example is given for illustration and to check the validity of the proposed approach.

Within the context of fractional calculus, we investigate novel mathematical possibilities. In this context, using the fractional dispersion relations for the fractional wave equation, we explore a class of the generalized fractional wave equation numerically. Some important classes of differential equations in the theory of wave studies are Drinfeld–Sokolov–Wilson and Shallow Water equations. In this effort, the natural transform decomposition technique has been implemented to investigate the explicit result of fractional-order coupled schemes of Drinfeld–Sokolov–Wilson and Shallow Water coupled systems. The proposed method is obtained by coupling the Natural transform with the Adomian decomposition process. The current technique significantly works to find the approximate solution without any discretization or constraining parameter assumptions. The obtained numerical and graphical outcomes by the devised technique are compared with the available exact result to verify the convergence of the method. For mathematical calculations, the Mathematica software package is used. [ABSTRACT FROM AUTHOR]

Emerging technology selection is crucial for enterprise integration, driving innovation, competitiveness, and streamlining operations across diverse sectors like finance and healthcare. However, the decision-making process for technology adoption is often complex and fraught with uncertainties. Bipolar fuzzy sets offer a nuanced representation of uncertainty, allowing for simultaneous positive and negative membership degrees, making them valuable in decision-making and expert systems. In this paper, we introduce dynamic averaging and dynamic geometric operators under bipolar fuzzy environment. We also establish some of the fundamental crucial features of these operators. Moreover, we present a step by step mechanism to solve MADM problem under bipolar fuzzy dynamic aggregation operators. In addition, these new techniques are successfully applied for the selection of the most promising emerging technology for enterprise integration. Finally, a comparative study is conducted to show the validity and practicability of the proposed techniques in comparison to existing methods. [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]

Chemical graph theory is an extension of mathematical chemistry that explores chemical phenomena and entities using the conceptual frameworks of graph theory. Chemical graph theory, in particular, uses chemical graphs to represent molecular structures. This chemical graph represents bonds and atoms, respectively, as edges and vertices. Cheminformatics predominantly uses chemical graphs as an essential data type for depicting chemical structures. The computable features of graphs lay the groundwork for (quantitative) structure-property and structure-activity predictions, which is an important component of cheminformatics. The physical and chemical characteristics of chemical compounds are thus reflected in these graphs, which can subsequently be reduced to graph-theoretical descriptors or indices. One of the most extensively researched distance-based graph descriptors is the resolving set W (metric dimension), which differentiates each pair of distinct vertices in every connected simple graph. The mixed metric dimension, which is the most significant variant of metric dimension, is determined for the complex molecular graph of a one-pentagonal carbon nanocone (PCN) in this manuscript. We show that only three distinct non-neighboring vertices (least possible requirement) from PCN can be adopted to uniquely identify all of the edges as well as vertices in PCN. [ABSTRACT FROM AUTHOR]

In this study, we introduce The p, q-quasirung orthopair fuzzy Dombi operators, including p, q-quasirung orthopair fuzzy Dombi weighted averaging (p, q-QOFDWA), p, q-quasirung orthopair fuzzy Dombi ordered weighted averaging (p, q-QOFDOWA), p, q-quasirung orthopair fuzzy Dombi weighted geometric (p, q-QOFDWG), and p, q-quasirung orthopair fuzzy Dombi ordered weighted geometric (p, q-QOFDOWG) operators. These operators effectively manage imprecise and uncertain information, outperforming other fuzzy sets like the Pythagorean fuzzy set (PFS) and q-rung orthopair fuzzy set (q-ROFS). We investigate their properties, including boundedness and monotonicity, and demonstrate their applicability in multiple criteria decision-making (MCDM) problems within a p, q-quasirung orthopair fuzzy (p, q-QOF) environment. To showcase the practicality, we present a real-world scenario involving the selection of investment alternatives as an illustrative example. Our findings highlight the significant advantage and potential of these operators for handling uncertainty in decision-making. [ABSTRACT FROM AUTHOR]

Fractional transportation problem that includes source and destination may have fractional objective functions in real-world applications to maximize the profitability ratio like profit/cost or profit/time. We refere to such transportation problems as fractional transportation problem. The paper considers the interval-valued neutrosophic numbers and its aritemematic operations. This paper deals with fractional transportation problem having discounting cost in neutrosophic environment, where the supply, demand and transportation costs are uncertain. The problem is considered by introducing all the parameters as neutrosophic numbers. Using the benefits of the score function definition, the problem is transformed into the corresponding deterministic form which can be illustrated by any method. and hence by applying of least cost method with the help of Kuhn-Tucker' optimality conditions, the optimal solution is resulted. Our strategy is to assess the issue and can rank different sort of neutrosophic numbers. To claify the proposed technique, a numerical example is given to show the adequacy of the new model. [ABSTRACT FROM AUTHOR]

The Decision-Theoretic Rough Set model stands as a compelling advancement in the realm of rough sets, offering a broader scope of applicability. This approach, deeply rooted in Bayesian theory, contributes significantly to delineating regions of minimal risk. Within the Decision-Theoretic Rough Set paradigm, the universal set undergoes a tripartite division, where distinct regions emerge and losses are intelligently distributed through the utilization of membership functions. This research endeavors to present an enhanced and more encompassing iteration of the Decision-Theoretic Rough Set framework. Our work culminates in the creation of the Generalized Intuitionistic Decision-Theoretic Rough Set (GI-DTRS), a fusion that melds the principles of Decision-Theoretic Rough Sets and intuitionistic fuzzy sets. Notably, this synthesis bridges the gaps that exist within the conventional approach. The innovation lies in the incorporation of an error function tailored to the hesitancy grade inherent in intuitionistic fuzzy sets. This integration harmonizes seamlessly with the contours of the membership function. Furthermore, our methodology deviates from established norms by constructing similarity classes based on similarity measures, as opposed to relying on equivalence classes. This shift holds particular relevance in the context of aggregating information systems, effectively circumventing the challenges associated with the process. To demonstrate the practical efficacy of our proposed approach, we delve into a concrete experiment within the information technology domain. Through this empirical exploration, the real-world utility of our approach becomes vividly apparent. Additionally, a comprehensive comparative analysis is undertaken, juxtaposing our approach against existing techniques for aggregation and decision modeling. The culmination of our efforts is a well-rounded article, punctuated by the insights, recommendations, and future directions delineated by the authors. [ABSTRACT FROM AUTHOR]