Supply chain logistics information collaboration (SCLIC) is the basis and premise of supply chain logistics collaboration. An effective logistics information collaboration strategy can improve enterprises’ chances of success in a new partnership in the supply chain. However, SCLIC is usually difficult to be achieved because of the complexity of the impact factor, strategy evolution process, and stability strategies. We propose an evolutionary game theory-based SCLIC strategy to solve this problem. Firstly, we build an evolutionary game model of SCLIC based on a two-echelon supply chain composed of suppliers and manufacturers. The strategy evolution game of suppliers, manufacturers, and the combination of manufacturers and suppliers are analyzed based on benefit maximization. The evolutionary stability strategy (ESS) is determined. Secondly, we analyze the impact of the relevant parameters on the strategy evolution process. And we use a comprehensive simulation to examine the efficiency of the proposed model. Finally, some managerial insights are presented for the supply chain enterprises to promote the realization of SCLIC.

In this paper, we seek to evaluate supply chain logistics information quality (SCLIQ) from the Just-In-Time (JIT) perspective. First, based on the analysis of SCLIQ, it is proposed that the SCLIQ evaluation should be combined with JIT philosophy. Second, based on SPC and information entropy method, an evaluation method of SCLIQ is presented. The statistical process control method (SPC) is adopted to evaluate the information quality of quantity and time respectively, the information entropy method is employed to determine their weight and the comprehensive evaluation results, and these results are analyzed according to SPC. Finally, a numerical example is used to demonstrate the feasibility of the proposed method. The major contribution of this paper is the combination of SQLIQ and JIT philosophy, while an objective and comprehensive evaluation method of SCLIQ from the JIT perspective is developed. The results are useful for evaluating SCLIQ and determining the best direction of improvement activities.

Promoting high-quality development of the manufacturing industry is the key to achieving sustainable economic development. The development of the manufacturing industry is affected by a variety of factors, and it is particularly important to reflect on its level of development from multiple perspectives. This paper built an evaluation indicator system for the manufacturing industry’s high-quality development using the structural equation model. An improved TOPSIS evaluation model was constructed based on the combined weighting of the level difference maximization method. The time series arithmetic mean operator was introduced to assign weight to time, and the quadratic weighted integration enabled a dynamic evaluation. The level of high-quality development of China’s manufacturing industry from 2018 to 2022 was assessed both statically and dynamically. According to the findings, the overall high-quality development of China’s manufacturing industry exhibited a trend of first declining and then increasing when the current status was only element taken into account. Regionally, there was a sequential decline from the east to the central part, followed by the western and northeastern regions. When focusing solely on the degree of growth, it was higher in the northeast and lower in the east. However, the regional characteristics declined when both the current status and the degree of growth were considered. Evaluating the high-quality development level of the manufacturing industry can provide references and lessons for promoting regional coordinated and sustainable development of China’s manufacturing industry.

Attribute reduction is a challenging issue in intelligent manufacturing. Existing methods are mainly based on rough set theory (RST) focusing on symbolic and discrete values. However, classical RST doesn't consider the complex interrelationship among conditional attributes and consecutive attribute values. Our article seeks to deal with this problem by proposing a hybrid framework based on generalized grey relational analysis (GGRA) and decision-making trial and evaluation laboratory (DEMATEL) method. GGRA is used to determine the initial importance of conditional attributes relative to the decision attribute and the direct relationship among conditional attributes. The causal relationships among conditional attributes are calculated by the DEMATEL method. Then we calculate the final importance of the conditional attribute relative to the decision attribute and apply a threshold to control the number of attributes entering the core set. Finally, an illustrative case and experiments are verified our method of attribute reduction with consecutive attribute values and complex interrelationship among attributes.

In consideration of the characteristics of two-stage (stable and degraded), nonlinearity and non-stationary randomness in the full life-cycle evolution process of the rolling bearing health indicator (HI), a novel remaining useful life (RUL) prediction method for rolling bearings is proposed based on long short-term memory network–Mahalanobis distance (LSTM-MD) and an incremental unscented Kalman filter (IUKF). First, an LSTM-MD hybrid algorithm is developed to precisely identify the critical change point (CP) between stable operation and incipient degradation in bearing HI trajectories, effectively mitigating the susceptibility of conventional threshold-based methods to HI fluctuations. Second, during the degradation stage, a degradation analysis model based on the nonlinear Wiener process is constructed. Simultaneously, an IUKF-based RUL prediction method for bearings is proposed, which overcomes the implicit assumption of the traditional UKF method that one-step prediction can replace state prediction, particularly in scenarios with significant HI fluctuations, thereby significantly reducing prediction errors. Finally, the proposed method is validated through comparisons with traditional methods using both the XJTU-SY public dataset and a self-built bearing test dataset. The results demonstrate that compared to traditional methods, the accuracy of initial degradation change point identification is improved by 32.6%, and the root mean square error (MSE) of RUL prediction is decreased by 41.8%.

According to the investigations, at this stage, the number of Chinese football volunteers is low; the propaganda of football volunteers needs to be strengthened; the activity and legal guarantee mechanism of football volunteers need to be improved, and a reasonable and comprehensive effective system has not been formed yet. Relevant departments shall formulate practical and feasible activity mechanisms in light of the actual local conditions; a systematic training system for football volunteers needs to be established.