Time series data for decision problems such as energy demand forecasting are often derived from uncertain assessments, and do not meet any statistical assumptions. The interval grey number becomes an appropriate representation for an uncertain and imprecise observation. In order to obtain nonlinear interval grey numbers with better forecasting accuracy, this study proposes a combined model by fusing interval grey numbers estimated by neural networks (NNs) and the grey prediction models. The proposed model first uses interval regression analysis using NNs to estimate interval grey numbers for a real valued sequence; and then a grey residual modification model is constructed using the upper and lower wrapping sequences obtained by NNs. It turns out that two different kinds of interval grey numbers can be estimated by nonlinear interval regression analysis. Forecasting accuracy on real data sequences was then examined by the best non-fuzzy performance values of the combined model. The proposed combined model performed well compared with the other interval grey prediction models considered.

Purpose: It is challenging to derive an appropriate tolerance relation for tolerance rough set-based classifiers (TRSCs). The traditional tolerance rough set employs a simple distance function to determine the tolerance relation. However, such a simple function does not take into account criterion weights and the interaction among criteria. Further, the traditional tolerance relation ignores interdependencies concerning direct and indirect influences among patterns. This study aimed to incorporate interaction and interdependencies into the tolerance relation to develop non-additive grey TRSCs (NG-TRSCs).Design/methodology/approach: For pattern classification, this study applied non-additive grey relational analysis (GRA) and the decision-making trial and evaluation laboratory (DEMATEL) technique to solve problems arising from interaction and interdependencies, respectively.Findings: The classification accuracy rates derived from the proposed NG-TRSC were compared to those of other TRSCs with distinctive features. The results showed that the proposed classifier was superior to the other TRSCs considered.Practical implications: In addition to pattern classification, the proposed non-additive grey DEMATEL can further benefit the applications for managerial decision-making because it simplifies the operations for decision-makers and enhances the applicability of DEMATEL.Originality/value: This paper contributes to the field by proposing the non-additive grey tolerance rough set (NG-TRS) for pattern classification. The proposed NG-TRSC can be constructed by integrating the non-additive GRA with DEMATEL by using a genetic algorithm to determine the relevant parameters.

The outbreak of COVID-19 at the beginning of 2020 had a significant impact on China’s economy, society, and citizens; it also had a negative impact on the development of the construction industry. In particular, small and medium-sized construction enterprises with low ability to withstand risk have been strongly impacted, aggravating a crisis of survival among these firms. The focus of this study is to analyze the impact of COVID-19 on the growth of small and medium-sized construction companies. Based on the characteristics of small and medium-sized construction enterprises, this paper establishes a growth evaluation index and builds a growth evaluation model based on factor analysis. Twenty-three construction enterprises listed on small and medium-sized enterprises board are selected as samples, and the quarterly data of 2019 and 2020 are used for empirical analysis. The results show that the epidemic has had a high short-term impact on construction enterprises, and the total output value of the construction industry in the first quarter of 2020 was 16% lower than that in the same period of last year. In the long run, the impact of the epidemic on the growth of small and medium-sized construction enterprises has been limited. In the first quarter of 2020, the growth score of enterprises decreased by only 1.95% year-over-year, and it was essentially flat in the second and third quarters. The epidemic has had little influence on solvency, tangible resources or intangible resources but a high short term influence on profitability, capital expansion and market expectations. The long-term impact is small; It is conducive to the improvement of enterprise operation ability. Finally, to both address the influence of the COVID-19 on small and medium-sized construction enterprises and to realize their transformation and upgrading, targeted suggestions are offered at the policy and enterprise levels. The results will help to understand the impact of the epidemic on the growth of construction enterprises, and provide decision support for the healthy and orderly development of the follow-up construction industry.

Intelligent terminal equipment is becoming extremely popular, and online learning has been increasingly accepted by people. In this context, it is particularly important to consider the ways of activating students’ interest in learning, improving their learning efficiency, and perception of network learning, as well as whether they can meet the requirements of the curriculum. The participants of this study are university students from China’s Fujian Province. According to the findings, it is suggested to improve the hybrid teaching resources, help teachers establish a correct outlook on learning, and improve their online learning management abilities.

Using software of finite element analysis LS-DYNA, in this paper we performed numerical simulation on the propagation law of shock waves in single layer spherical lattice shell under the effect of internal explosion, obtained the laws of propagation, reflection, convergence and peak appeared of shock waves, concluded that the damage of enclosed structure is closely related to, initial overpressure peak, and partial secondary overpressure peak, analyzed the influences of explosive quantity, explosion center height, and explosive eccentric position on the propagation law of shock waves. Based on the results of numerical simulation, we concluded joint area between lattice shell top and wall shell is the key location for explosion venting and pointed out the calculation formula of overpressure peak on structure surface of higher fitting precision is feasible.