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In this paper, community identification has been considered as the most critical task of social network analysis. The purpose of this paper is to organize the nodes of a given network graph into distinct clusters or known communities. These clusters will therefore form the different communities available within the social network graph.

To date, numerous methods have been developed to detect communities in social networks through graph clustering techniques. The k-means algorithm stands out as one of the most well-known graph clustering algorithms, celebrated for its straightforward implementation and rapid processing. However, it has a serious drawback because it is insensitive to initial conditions and always settles on local optima rather than finding the global optimum. More recently, clustering algorithms that use a reciprocal KNN (k-nearest neighbors) graph have been used for data clustering. It skillfully overcomes many major shortcomings of k-means algorithms, especially about the selection of the initial centers of clusters. However, it does face its own challenge: sensitivity to the choice of the neighborhood size parameter k, which is crucial for selecting the nearest neighbors during the clustering process. In this design, the Jaya optimization method is used to select the K parameter in the KNN method.

The experiment on real-world network data results show that the proposed approach significantly improves the accuracy of methods in community detection in social networks. On the other hand, it seems to offer some potential for discovering a more refined hierarchy in social networks and thus becomes a useful tool in the analysis of social networks.

This paper introduces an enhancement to the KNN graph-based clustering method by proposing a local average vector method for selecting the optimal neighborhood size parameter k. Furthermore, it presents an improved Jaya algorithm with KNN graph-based clustering for more effective community detection in social network graphs.

The purpose of this study is to model steam condensing flows through steam turbine blades and find the most suitable condensation model to predict the condensation phenomenon.

To find the most suitable condensation model, five nucleation equations and four droplet growth equations are combined, and 20 cases are considered for modelling the wet steam flow through steam turbine blades. Finally, by the comparison between the numerical results and experiments, the most suitable case is proposed. To find out whether the proposed case is also valid for other boundary conditions and geometries, it is used to simulate wet steam flows in de Laval nozzles.

The results indicate that among all the cases, combining the Hale nucleation equation with the Gyarmathy droplet growth equation results in the smallest error in the simulation of wet steam flows through steam turbine blades. Compared with experimental data, the proposed model’s relative error for the static pressure distribution on the blade suction and pressure sides is 2.7% and 2.3%, respectively, and for the liquid droplet radius distribution it totals to 1%. This case is also reliable for simulating condensing steam flows in de Laval nozzles.

The selection of an appropriate condensation model plays a vital role in the simulation of wet steam flows. Considering that the results of numerical studies on condensation models in recent years have not been completely consistent with the experiments and that there are still uncertainties in this field, further studies aiming to improve condensation models are of particular importance. As condensation models play an important role in simulating the condensation phenomenon, this research can help other researchers to better understand the purpose and importance of choosing a suitable condensation model in improving the results. This study is a significant step to improve the existing condensation models and it can help other researchers to gain a revealing insight into choosing an appropriate condensation model for their simulations.

This paper aims to investigate the three-dimensional (3D) numerical simulations, based on the Navier–Stokes equations and the energy equation. Forced convection of a mixture of (60:40) percent ethylene glycol and water, was used as the base fluid and CuO nanoparticles, through a serpentine minichannel.

In this simulation, a serpentine mini-channel heat exchanger was simulated. The fluid studied in this simulation was composed of a mixture of (60:40) per cent ethylene glycol and water, was used as the base fluid and CuO nanoparticles. Four slabs and three serpentines were used in this study. The serpentine section is connected to the slab. Three equidistant circular channels (1 mm in diameter) were implemented inside the slab.

Results show that nanoparticles increase the fluid pressure drop and by changing volume fraction of nanoparticles from 0 to 1 per cent, the pressure drop of nanofluids increases between 42and 47 per cent, for Reynolds numbers from 100 to 500. The existence of serpentine bend in the minichannel heat exchanger causes the heat transfer rate to increase. Increase the volume fraction of nanoparticles reduces the fluid temperature at the outlet of the heat exchanger. The numerical results show that in Re = 500, at the beginning of the last slab in middle channel by changing volume fraction of nanoparticles from 0 to 2 per cent, local Nusselt number 57.40 per cent increase. The existence of the serpentine bend causes the heat transfer rate to increase.

Forced convection of a mixture of (60:40) per cent ethylene glycol and water by using of 3D numerical simulations, based on the Navier–Stokes equations.

This study aims to examine the effect of a combination of hybrid pin-fin patterns on a heat sink's performance using numerical techniques. Also, flow characteristics have been studied, such as secondary flow formation and flow-wall interaction.

In this study, the effect of hybrid arrangements of elliptical and hexagonal pin-fins with different distribution percentages on flow characteristics and performance evaluation criteria in laminar flow was investigated. Ansys-Fluent software solves the governing equations using the finite volume method. Also, the accuracy of obtained results was compared with the experimental results of other similar papers.

The results of this study highlighted that hybrid arrangements show higher overall performance than single pin-fin patterns. Among the hybrid arrangements, case 3 has the highest values of performance evaluation criteria, that is, 1.84 in Re = 900. The results revealed that, with the instantaneous change in the pattern from elliptic to hexagonal, the secondary flow increases in the cross-sectional area of the channels, and the maximum velocity in the cross-section of the channel increases. The important advantages of case 3 are its highest overall performance and a lower chip surface temperature of up to about 2% than other hybrid patterns.

Prior research has shown that in the single pin-fin pattern, the cooling power at the end of the heat sink decreases with increasing fluid temperature. Also, a review of previous studies showed that existing papers had not investigated hybrid pin-fin patterns by considering the effect of changing distribution percentages on overall performance, which is the aim of this paper.

The current study investigates the role of consumer ethnocentrism (CE) in the context of locally produced organic food. This research work further extends the extended theory of planned behavior (TPB) model by examining the mediating effect of extended TPB variables (ATT, SN, PBC, PV) between CE and PI for locally produced organic food.

Data were obtained from the visitors of two shopping malls situated in a large, heavily populated city of India using survey method. Further, two-step approach was applied to analyze the hypothesized model.

Findings indicate that CE is a substantial determinant of PI for locally produced organic food. Moreover, extended TPB mediates the relation between CE and PI for locally produced organic food.

Post Covid-19, market size of organic food is rapidly growing in India. In this regard, this study presents useful implications for the marketers of organic food for gaining better consumer insights to further develop appropriate marketing strategies.

CE has been found to be a useful predictor of their food attitudes. However, studies, investigating the role of CE in the context of sustainable food consumption, are scant. Moreover, studies exploring the mediating effect of extended TPB variables are also very limited.