Search

The concept of being uniform strong interval-valued intuitionistic fuzzy (also termed as USIVIF) is an integration of two ideologies, which are called "uniformity" and "strong interval-valued intuitionistic fuzzy sets". Inspired by the study on uniform fuzzy topological indices, it is natural to introduce uniform IVIFTIs. Originally, topological indices were generalized for the fuzzy sets However, the utilization of the interval-valued intuitionistic fuzzy topological indices provides a finer approach, especially if there are multiple uncertainties based on intervals. Consequently, both theories imply that topological indices are not fixed and depend on certain situations or problems in the question. In this article, the generalized results for the uniform degree of the fuzzy sets associated with individual vertices/edges of strong interval-valued intuitionistic fuzzy graphs were presented and results for the total uniform degree of such graphs were also included. In addition, the nature of the implemented methods and models was discussed based on the cellular neural interval-valued intuitionistic fuzzy graphs of sets of membership and non-membership values.

This work presents a simulation of the transient cold energy storage process in a porous container, accounting for both radiation and conduction mechanisms. The Galerkin method is applied to model the intricate system interactions, while adaptive mesh refinement increases the accuracy of the numerical simulations. This method provides an in-depth analysis of factors affecting the solidification process. Key findings indicate a substantial reduction in freezing time, as radiation accelerates the solidification period by roughly 57.44 %. Adding nanoscale particles to water further enhances freezing speed, reducing the time by approximately 6.25 %. Moreover, the inclusion of a porous medium significantly boosts thermal conduction, cutting freezing time by an impressive 67.48 %. By combining radiation, conduction, and advanced materials into a unified model. The findings underscore the effectiveness of these enhancements in improving freezing performance, providing valuable insights for advancing cold energy storage systems. This work advances cold storage technology and establishes a high standard for simulation accuracy and process optimization.

Incorporating thermoelectric (TE) modules into photovoltaic/thermal (PVT) systems can markedly increase energy output by improving the overall efficiency of energy conversion. This research focused on the design and simulation of a PVT system integrated with a TE module, utilizing ANSYS Fluent. The study assessed four different tube cross-sectional shapes—circular, square, elliptical, and triangular—all with the same cross-sectional areas. Moreover, the investigation included the impact of Cu-alumina/H2O hybrid nanofluid at a 0.024 % volume concentration, fluid inlet velocity (ui), and solar radiation (G) on PV temperature (TPV) and the overall productivity. The outputs showed that the triangular configuration considerably reduced TPV compared to the other shapes. This configuration also generated the highest thermal power, reaching 130.84 W. Additionally, at ui = 0.19 m/s, the unit's thermal efficiency and overall electrical efficiency increased by 0.93 % and 0.22 %, respectively.

This work employs an approach to examine the effects of adding alumina nanoparticles with varying diameters (dp) to water on the productivity of cold storage. Utilizing a finned geometry with a curved cold wall, the mesh configuration was adapted to the solid front's location, and unsteady terms were discretized using implicit methods. The concentration (ϕ) of alumina nanoparticles was also evaluated as a key parameter. Results indicated that increasing the concentration of nanoparticles improved cold storage efficiency by approximately 42.11 %. Among the various sizes, medium-sized nanoparticles were most effective, yielding a 20 % improvement in freezing efficiency compared to the smallest size. Optimal nanoparticle dispersion significantly reduced the required cooling time from 9383.58 s to 5507.129 s. This study highlights the substantial benefits of combining fins and nanoparticles to enhance cold storage systems, presenting a notable advancement in thermal management technology.

The zero-divisor graph of a commutative ring R with a nonzero identity, denoted by Γ(R), is an undirected graph where the vertex set Z(R)* consists of all nonzero zero-divisors of R. Two distinct vertices a and b in Γ(R) are adjacent if and only if ab=0. The normalized Laplacian spectrum of zero-divisor graphs has been studied extensively due to its algebraic and combinatorial significance. Notably, Pirzada and his co-authors computed the normalized Laplacian spectrum of Γ(Zn) for specific values of n in the set {pq,p2q,p3,p4}, where p and q are distinct primes satisfying p

Chemical graph theory, a branch of computational and applied mathematics, covers a very wide range of topics. As a result, the world of applied sciences heavily relies on graph theory. The concept of partition dimension has significant importance in the field of chemical graph theory. Although certain graphs have bounded partition dimensions, a graph’s partition dimension may be constant. In this study, we look at two alternative chemical structures made of an octagonal grid: nanosheets and nanotubes. We determined the partition dimension of an octagonal grid-generated nanosheet to be 3, and the partition dimension of a nanotube to be limited from 4.

Graphs give a mathematical model of molecules, and thery are used extensively in chemical investigation. Strategically selections of graph invariants (formerly called "topological indices" or "molecular descriptors") are used in the mathematical modeling of the physio-chemical, pharmacologic, toxicological, and other aspects of chemical compounds. This paper describes a new technique to compute topological indices of two types of chemical networks. Our research examines the mathematical characteristics of molecular descriptors, particularly those that depend on graph degrees. We derive a compact mathematical analysis and neighborhood multiplicative topological indices for product of graphs (L) and tetrahedral diamond lattices (Ω). In this paper, the fifth multiplicative Zagreb index, the general fifth multiplicative Zagreb index, the fifth multiplicative hyper-Zagreb index, the fifth multiplicative product connectivity index, the fifth multiplicative sum connectivity index, the fifth multiplicative geometric-arithmetic index, the fifth multiplicative harmonic index and the fifth multiplicative redefined Zagreb index are determined. The comparison study of these topological indices is also discussed.

This work considers a discrete-time predator-prey system with a strong Allee effect. The existence and topological classification of the system's possible fixed points are investigated. Furthermore, the existence and direction of period-doubling and Neimark-Sacker bifurcations are explored at the interior fixed point using bifurcation theory and the center manifold theorem. A hybrid control method is used for controlling chaos and bifurcations. Some numerical examples are presented to verify our theoretical findings. Numerical simulations reveal that the discrete model has complex dynamics. Moreover, it is shown that the system with the Allee effect requires a much longer time to reach its interior fixed point.

In November 2019, there was the first case of COVID-19 (Coronavirus) recorded, and up to 3rd of April 2020, 1,116,643 confirmed positive cases, and around 59,158 dying were recorded. Novel antiviral structures of the SARS-COV-2 virus is discussed in terms of the metric basis of their molecular graph. These structures are named arbidol, chloroquine, hydroxy-chloroquine, thalidomide, and theaflavin. Partition dimension or partition metric basis is a concept in which the whole vertex set of a structure is uniquely identified by developing proper subsets of the entire vertex set and named as partition resolving set. By this concept of vertex-metric resolvability of COVID-19 antiviral drug structures are uniquely identified and helps to study the structural properties of structure.

A two-mode network is a type of network in which nodes can be divided into two sets in such a way that links can be established between different types of nodes. The relationship between two separate sets of entities can be modeled as a bipartite network. In computer networks data is transmitted in form of packets between source to destination. Such packet-switched networks rely on routing protocols to select the best path. Configurations of these protocols depends on the network acquirements; that is why one routing protocol might be efficient for one network and may be inefficient for a other. Because some protocols deal with hop-count (number of nodes in the path) while others deal with distance vector. This paper investigates the minimum transmission in two-mode networks. Based on some parameters, we obtained the minimum transmission between the class of all connected n-nodes in bipartite networks. These parameters are helpful to modify or change the path of a given network. Furthermore, by using least squares fit, we discussed some numerical results of the regression model of the boiling point in benzenoid hydrocarbons. The results show that the correlation of the boiling point in benzenoid hydrocarbons of the first Zagreb eccentricity index gives better result as compare to the correlation of second Zagreb eccentricity index. In case of a connected network, the first Zagreb eccentricity index ξ1(ℵ) is defined as the sum of the square of eccentricities of the nodes, and the second Zagreb eccentricity index ξ2(ℵ) is defined as the sum of the product of eccentricities of the adjacent nodes. This article deals with the minimum transmission with respect to ξi(ℵ), for i=1,2 among all n-node extremal bipartite networks with given matching number, diameter, node connectivity and link connectivity.