The research on high-quality development of the manufacturing industry (HQDMI) in the Yellow River Basin is in the exploratory stage, and little literature has systematically analyzed its influencing factors. Based on the grounded theory, this paper makes an exploratory analysis of the influencing factors of the HQDMI in the Yellow River Basin. This study finds that the influencing factors of the HQDMI mainly include six aspects: innovation capability, organization construction, industry resources, institutional environment, industry collaboration, and market demand. By using the HQDMI’s endogenous and exogenous factors as the core, the realization mode of the HQDMI in the Yellow River basin is put forward, and the influencing factors and action model of the HQDMI in the Yellow River Basin are constructed. The findings have significant reference value for promoting the HQDMI in the Yellow River Basin and are helpful for improving the HQDMI research.

Scientific and accurate evaluations of the high-quality development level of the manufacturing industry have important theoretical significance and reference value for the government and for decision-making departments for the formulation of corresponding incentive measures. Firstly, based on rough set theory, this paper proposes an attribute reduction method, which can help to delete redundant indexes and reduce the calculation workload. Secondly, a more scientific combination weighting method is proposed, and the calculation method of the total index in an evaluation index system is given. Finally, the HQDMI evaluation index system is constructed based on the connotations of the high-quality development of the manufacturing industry. Taking China as an example, the total index and sub-index of high-quality development of 30 provinces in China are calculated, the high-quality development level of the manufacturing industry in 30 provinces is clustered based on SPSS24.0, and the visualization of the clustering results is achieved by ArcGIS software. The results show that the high-quality development level of China’s manufacturing industry has regional distribution characteristics. Regions with high development levels are mainly distributed in eastern coastal areas, followed by the central development level, and those of the northeast and west are low. This study provides a theoretical application mode for the evaluation of the high-quality development level of the manufacturing industry, and it has theoretical guidance significance for promoting the high-quality development of the manufacturing industry.

Purpose: To address challenges in forecasting seasonal time series, especially in agricultural drought forecasting, this paper proposes the discrete interaction grey forecasting model with dual perspective coupled seasonal dummy variables (DS-DVGM(1,1,T)). Design/methodology/approach: The DS-DVGM(1,1,T) model is built upon the theoretical framework of the discrete grey forecasting model, incorporating a dual processing mechanism to account for the dual interaction effect and nonlinear characteristics of the system. Seasonal dummy variables are introduced to portray the fluctuating tendency of seasonal series. The parameter estimation method for the new model is discussed, and the Differential Evolutionary algorithm is used to solve the linear and hyper-parameters of the model. Findings: The DS-DVGM(1,1,T) model outperforms other forecasting models, accurately describing the seasonal pattern of soil moisture in six cities of Henan Province, with forecast results closely matching actual drought conditions. Practical implications: The DS-DVGM(1,1,T) model provides a reliable forecasting benchmark for agricultural drought monitoring. By accurately forecasting drought conditions, farmers and policymakers can take timely and appropriate actions to mitigate the impacts of drought on agriculture and grain security. Originality/value: The DS-DVGM(1,1,T) model offers a new perspective and methodology, overcoming the limitations of existing models in capturing seasonal trends and nonlinear features, aiding agricultural drought monitoring and management.

Purpose: The possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems with the choice dilemma of the maximum criteria and instances when the possibility function may not accurately capture the data's randomness. This study aims to propose a multi-stage skewed grey cloud clustering model that blends grey and randomness to overcome these problems.Design/methodology/approach: First, the skewed grey cloud possibility (SGCP) function is defined, and its digital characteristics demonstrate that a normal cloud is a particular instance of a skewed cloud. Second, the border of the decision paradox of the maximum criterion is established. Third, using the skewed grey cloud kernel weight (SGCKW) transformation as a tool, the multi-stage skewed grey cloud clustering coefficient (SGCCC) vector is calculated and research items are clustered according to this multi-stage SGCCC vector with overall features. Finally, the multi-stage skewed grey cloud clustering model's solution steps are then provided.Findings: The results of applying the model to the assessment of college students' capacity for innovation and entrepreneurship revealed that, in comparison to the traditional grey clustering model and the two-stage grey cloud clustering evaluation model, the proposed model's clustering results have higher identification and stability, which partially resolves the decision paradox of the maximum criterion.Originality/value: Compared with current models, the proposed model in this study can dynamically depict the clustering process through multi-stage clustering, ensuring the stability and integrity of the clustering results and advancing grey system theory.

Purpose: This paper constructs a grey matrix correlation model to thoroughly investigate the integration mechanism between the digital and manufacturing industries and to objectively evaluate their correlation within the Yellow River Basin. Design/methodology/approach: First, based on spatial geometry and calculus theory, the surface representation method of behavioral matrices in three-dimensional space is explained, and the calculation method for the volume of the corresponding surface body is provided. Second, inspired by the two-dimensional grey absolute correlation model, a grey matrix correlation model based on the volume of the surface body is established. We analyze the properties of the proposed model, including normalization, proximity, and order preservation under scalar multiplication. Additionally, the MATLAB implementation code for the model is provided to enhance its operability. Finally, the model is applied to empirically analyze the correlation between manufacturing and the digital industry in the Yellow River Basin. Findings: The results validate the effectiveness and rationality of the model, and policy recommendations are proposed to optimize resource allocation and enhance the digitalization level of manufacturing in the region. Originality/value: This study provides a theoretical basis and methodological support for promoting the high-quality development of manufacturing in the Yellow River Basin, while also offering insights for other regions exploring pathways for the integration of the two industries.