The evolution of compact density heat gadgets demands effective thermal transportation. The notion of nanofluid plays active role for this requirements. A comparative account for Maxwell nanofluids and Williamson nanofluid is analyzed. The bioconvection of self motive microorganisms, non Fourier heat flux and activation energy are new aspects of this study. This article elaborates the effects of viscous dissipation, Cattaneo–Christov diffusion for Maxwell and Williamson nanofluid transportation that occurs due to porous stretching sheet. The higher order non-linear partial differential equations are solved by using similarity transformations and a new set of ordinary differential equations is formed. For numerical purpose, Runge–Kutta method with shooting technique is applied. Matlab plateform is used for computational procedure. The graphs for various profiles .i.e. velocity, temperature, concentration and concentration of motile micro-organisms are revealed for specific non-dimensional parameters. It is observed that enhancing the magnetic parameter M, the velocity of fluid decreases but opposite behavior happens for temperature, concentration and motile density profile. Also the motile density profile decrease down for Pe and Lb. The skin friction coefficient is enhanced for both the Williamson and Maxwell fluid.

In machine learning and data mining, feature selection (FS) is a traditional and complicated optimization problem. Since the run time increases exponentially, FS is treated as an NP-hard problem. The researcher's effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios. This paper presents two binary variants of a Hunger Games Search Optimization (HGSO) algorithm based on V-and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset. The proposed technique transforms the continuous HGSO into a binary variant using V-and S-shaped transfer functions (BHGSO-V and BHGSO-S). To validate the accuracy, 16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary algorithms. The findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features, classification accuracy, run time, and fitness values than other state-of-the-art algorithms. The results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification problems. The proposed BHGSO-V achieves 95% average classification accuracy for most of the datasets, and run time is less than 5 sec. for low and medium dimensional datasets and less than 10 sec for high dimensional datasets.

Metal organic graphs are hollow structures of metal atoms that are connected by ligands, where metal atoms are represented by the vertices and ligands are referred as edges. A vertex x resolves the vertices u and v of a graph G if d u , x ≠ d v , x . For a pair u , v of vertices of G , R u , v = x ∈ V G : d x , u ≠ d x , v is called its resolving neighbourhood set. For each pair of vertices u and v in V G , if f R u , v ≥ 1 , then f from V G to the interval 0,1 is called resolving function. Moreover, for two functions f and g , f is called minimal if f ≤ g and f v ≠ g v for at least one v ∈ V G . The fractional metric dimension (FMD) of G is denoted by dim f G and defined as dim f G = min g : g   is a minimal resolving function of  G , where g = ∑ v ∈ V G g v . If we take a pair of vertices u , v of G as an edge e = u v of G , then it becomes local fractional metric dimension (LFMD) dim l f G . In this paper, local fractional and fractional metric dimensions of MOG n are computed for n ≅ 1 mod 2 in the terms of upper bounds. Moreover, it is obtained that metal organic is one of the graphs that has the same local and fractional metric dimension.

Nowadays, the study of source localization in complex networks is a critical issue. Localization of the source has been investigated using a variety of feasible models. To identify the source of a network’s diffusion, it is necessary to find a vertex from which the observed diffusion spreads. Detecting the source of a virus in a network is equivalent to finding the minimal doubly resolving set (MDRS) in a network. This paper calculates the doubly resolving sets (DRSs) for certain convex polytope structures to calculate their double metric dimension (DMD). It is concluded that the cardinality of MDRSs for these convex polytopes is finite and constant.

Toeplitz networks are used as interconnection networks due to their smaller diameter, symmetry, simpler routing, high connectivity, and reliability. The edge metric dimension of a network is recently introduced, and its applications can be seen in several areas including robot navigation, intelligent systems, network designing, and image processing. For a vertex s and an edge g = s 1 s 2 of a connected graph G , the minimum number from distances of s with s 1 and s 2 is called the distance between s and g . If for every two distinct edges s 1 , s 2 ∈ E G , there always exists w 1 ɛ W E ⊆ V G , such that d s 1 , w 1 ≠ d s 2 , w 1 ; then, W E is named as an edge metric generator. The minimum number of vertices in W E is known as the edge metric dimension of G . In this study, we consider four families of Toeplitz networks T n 1,2 , T n 1,3 , T n 1,4 , and T n 1,2,3 and studied their edge metric dimension. We prove that for all n ≥ 4 , e dim T n 1,2 = 4 , for n ≥ 5 , e dim T n 1,3 = 3 , and for n ≥ 6 , e dim T n 1,4 = 3 . We further prove that for all n ≥ 5 , e dim T n 1,2,3 ≤ 6 , and hence, it is bounded.