In this paper, we investigate the concepts of the weighted average operator (A W ) and weighted geometric operator (G W ) on neutrosophic cubic sets (NCSs) to aggregate the neutrosophic cubic information. Moreover, on the basis of A W & G W and certain functions, including score, certainty and accuracy, we develop our algorithm to multiple-criteria decision making in NCSs, in which the assessment standards of another possibility on the characteristics yield the technique of neutrosophic cubic numbers (NCNs) to choice greatest necessary ones. Finally, we provide a mathematical example of the technique to determine the application and usefulness of the established technique.

In recent years, a mathematical approach of blending different aspects is on the way, which as a result gives a more generalized approach. Following the above mathematical approach, we combine two very powerful techniques, namely complex intuitionistic fuzzy sets and graph theory, and introduce the notion of complex intuitionistic fuzzy graphs. Then, we introduce certain notions including union, join and composition of complex intuitionistic fuzzy graphs, through which one can easily manipulate the complex intuitionistic fuzzy graphs in decision making problems. We elucidate these operations with some examples. We also describe the homomorphisms of complex intuitionistic fuzzy graphs. Finally, we provide an application in cellular network provider companies for the testing of our approach.

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In recent years, many supplementary designs have been developed incorporating the assumption that data follow the particular non-normal distribution. The-control chart is one such design proposed to monitor the parameter of the single parameter Rayleigh distributed data. Commonly, in authentic situations, practitioners need to estimate the scale parameter in the observed processes instead of. However, the positive square root of the-statistic used in the existing design of-control chart is not an unbiased estimator of and thus could not be practiced to monitor the scale parameter of the Rayleigh distributed process. A new structure of the-control chart namely for monitoring the scale parameter of the Rayleigh distributed data has been originally developed in this study. The statistical basis of this newly design in terms of average run length (), characteristic function and power curve have been derived. The analytical results are utilized further to determine the parameters of-chart and in comparing the performance of the proposed control chart with existing competitors. Comparative results illustrate the effectiveness of the proposed design in view of statistical power. Finally, the computational procedure of this newly-chart has been demonstrated using simulated data and real data on the breaking strength of carbon fibers.

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