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Abstract:Collaborative innovation in a supply chain cooperative network can improve the performance of the enterprise. However, how to achieve the sustainable and stable improvement of the enterprise synergy innovation in a supply chain cooperative network is a common topic of research. Based on a survey of 236 enterprises in 53 supply chain cooperative networks, this study found: (1) Integrative leadership has a positive impact on the enterprise synergy innovation performance in a supply chain cooperative network; (2) Knowledge integration and network relationship embeddedness play partial mediating roles between integrative leadership and the enterprise synergy innovation performance, respectively; (3) Knowledge integration and network relationship embeddedness play a sequenced mediating role between integrative leadership and the enterprise synergy innovation performance; (4) The partial mediation role of knowledge integration and network relationship embeddedness are of no significant difference; however, their single mediating roles are greater than that of the sequenced mediating role of knowledge integration and network relationship embeddedness. This paper further emphasizes the key role of the core organization leadership in a cooperative network and discusses its functional route, which is of great importance in developing the theory system of leadership and providing guidance for the cooperation of the supply chain.

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Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models. Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the first time by a research team of Nanjing University. This algorithm was proved to be more accurate and robust in comparison with other machine learning algorithms. Motivated by this benefit, this study employs the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm. To verify the superiority of our proposed method, several other machine learning algorithm-based perdition models were implemented to predict traffic accident severity with the same dataset, and the prediction results show that the Deep Forests algorithm present good stability, fewer hyper-parameters, and the highest accuracy under different level of training data volume. It is expected that the findings from this study would be helpful for the establishment or improvement of effective traffic safety system within a sustainable transportation system, which is of great significance for helping government managers to establish timely proactive strategies in traffic accident prevention and effectively improve road traffic safety.

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Due to the requirements of challenging planetary exploration missions with wheeled mobile robots (WMRs), the driving mechanics of WMRs’ wheels moving on the deformable terrain has been researched intensively, but the mechanics of the wheels’ steering is lacking research. Systematic steering experiments were carried out using a single-wheel testbed for wheels moving on a lunar soil simulant with different radii, widths, lug heights, and lug numbers under different vertical loads. The influence of the eccentric distance and motion state, such as the steering motor’s angular velocity, steering angle, and initial wheel sinkage, were also studied. The experimental results are illustrated with plenty of figures and analyzed based on the preliminary steering mechanics model to draw conclusions. The steering resistance moment is caused by the lateral bulldoze stress and the shearing stress at the bottom of the wheel. The wheel sinkage and steering moment of resistance increase with an increase in steering angle, which could be fitted with exponential functions. The steering moment is the increasing function of the wheel sinkage, eccentric distance, vertical load, and wheel width. The conclusions, empirical models, and experimental data can be taken as references to the optimal design of a steering mechanism and the development/verification of a wheel’s steering mechanics model.

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