In cluster synchronization (CS), the constituents (i.e., multiple agents) are grouped into a number of clusters in accordance with a function of nodes pertaining to a network structure. By designing an appropriate algorithm, the cluster can be manipulated to attain synchronization with respect to a certain value or an isolated node. Moreover, the synchronization values among various clusters vary. The main aim of this study is to investigate the asymptotic and CS problem of coupled delayed complex-valued neural network (CCVNN) models along with leakage delay in finite-time (FT). In this paper, we describe several sufficient conditions for asymptotic synchronization by utilizing the Lyapunov theory for differential systems and the Filippov regularization framework for the realization of finite-time synchronization of CCVNNs with leakage delay. We also propose sufficient conditions for CS of the system under scrutiny. A synchronization algorithm is developed to indicate the usefulness of the theoretical results in case studies.

Pythagorean neutrosophic set is an extension of a neutrosophic set which represents incomplete, uncertain and imprecise details. Pythagorean neutrosophic graphs (PNG) are more flexible than fuzzy, intuitionistic, and neutrosophic models. PNG are similar in structure to fuzzy graphs but the fuzziness is more resilient when compared with other fuzzy models. In this article, regular Pythagorean neutrosophic graphs are studied, where for each element the membership $(\mathfrak{M})$, and non-membership $(\mathfrak{N}\mathfrak{M})$ are dependent and indeterminacy $(\mathfrak{I})$ is independently assigned. The new ideas of regular, full edge regular, edge regular, and partially edge regular Pythagorean Neutrosophic graphs are introduced and their properties are investigated. A new MCDM method has been introduced using the Pythagorean neutrosophic graphs and an illustrative example is given by applying the proposed MCDM method.