No abstract

Innovation and entrepreneurship education is a key way to cultivate applied talents in colleges and universities. This article aimed to optimize the teaching quality of the Fundamentals of Entrepreneurship courses by constructing a conceptual model of “teaching quality–student satisfaction.”Teaching quality is divided into four indicator elements, teaching content, teaching methods, teaching conditions, and teaching management. A student satisfaction questionnaire was designed to measure the teaching quality of a Fundamentals of Entrepreneurship course. The reliability and validity of survey data from Linyi University were analyzed using SPSS.20 software. Through correlation and multiple regression analysis, it can be seen that the teaching content, teaching methods, teaching conditions and teaching management have a significant positive correlation with the teaching quality of the Fundamentals of Entrepreneurship course, which is an important factor affecting student’s satisfaction, and there is a certain gap between the expectation of teaching quality and student’s satisfaction. On this basis, suggestions are put forward to improve student satisfaction with the teaching quality of Fundamentals of Entrepreneurship courses, and to provide empirical evidence and recommendations for continuously improving the teaching quality of Fundamentals of Entrepreneurship courses, thereby improving college students’ employment and entrepreneurship ability.

No abstract

Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the “NASA prognostics data repository” are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models.

Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments, and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents’ dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.