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The so-called wall hanging is actually a decorative picture hung indoors. It can also be said that painting, printing, dyeing, weaving, and embroidery on the fabric. The difference between traditional Chinese painting, watercolor painting, and oil painting lies in the fact that it is made in real objects with a material sense of texture, quality, and luster; Secondly, it has a purely decorative purpose; Therefore, it must be coordinated with the entire interior design. Judging from the current situation of production of wall hangings in China, most of them are high-end products such as wool weaving, flannelette embroidery, and embroidery, which are exclusively for export. The theme content is mostly taken from scenic spots and historical sites. The silk paintings unearthed in Mawangdui, Changsha, can also be regarded as the earliest wall hangings in China. Decorative wall hanging is an increasingly important item in modern interior decoration, and good decorative wall hanging is often regarded as the "finishing touch" in interior decoration design; It can refresh and brighten the indoor environment, and is an indispensable and important part of modern indoor overall design. The decorative wall hanging design effect evaluation is often considered as a multi-attribute decision making (MADM) problem. In this paper, the grey relational analysis (GRA) decision method is extended to the neutrosophic cubic sets (NCSs) setting to deal with MAGDM. First, the NCSs are reviewed. Then, the CRITIC is used to obtain the weights of attributes. In addition, the neutrosophic cubic number GRA (NCN-GRA) is established for MADM, and the computational steps for all designs are listed. Finally, the decorative wall hanging design effect evaluation is given to demonstrate the NCN-GRA model and some good comparative analysis is done to demonstrate the advantages of NCN-GRA. [ABSTRACT FROM AUTHOR]

The modeling and prediction of short-term traffic flow can reflect the prediction results of the traffic state and traffic flow data. In this paper, first, we use a high-dimensional tensor to represent the multi-mode characteristics of traffic flow data, and we make use of the basic operations properties of tensors, such as Tucker decomposition, to study the methods for filling in data, such as ITRM. Additionally, we preprocess the lost traffic flow and abnormal data. At the same time, we study the short-term traffic flow based on the "week-day-time" multi-mode of the traffic flow data. Using the grey model (GM (1, 1)) to predict the same period of the weekly mode, the scrolling grey model (SGM) of the same time period is predicted. For the time mode, a neural network time series of wavelet analysis is used to predict the traffic flow forecast during the same period. Then, the prediction results of the three different models are weighted by the grey correlation analysis method, and then, the coupling prediction model of the three models is obtained. In the end, according to the traffic flow data of the main road of Shaoshan road in Changsha, Hunan, China, we first preprocess the lost data by using the filling algorithm for the tensor data, and then, we make the traffic flow data complete, use the three tensor data modes of traffic flow, and analyze the results. The experimental results show that the coupling prediction model with the tensor model is much better than the single GM (1, 1) model, the SGM and the neural network prediction model. [ABSTRACT FROM AUTHOR]