Lower limb exoskeleton robots offer an effective treatment for patients with lower extremity dysfunction. In order to improve the rehabilitation training effect based on the human motion mechanism, this paper proposes a humanoid sliding mode neural network controller based on the human gait. A humanoid model is constructed based on the human mechanism, and the parameterised gait trajectory is used as target to design the humanoid control system for robots. Considering the imprecision of the robot dynamics model, the neural network is adopted to compensate for the uncertain part of the model and improve the model accuracy. Moreover, the sliding mode control in the system improves the response speed, tracking performance, and stability of the control system. The Lyapunov stability analysis proves the stability of the control system theoretically. Meanwhile, an evaluation method using the similarity function is improved based on joint angle, velocity, and acceleration to evaluate the comfort of humans in rehabilitation training more reasonably. Finally, to verify the effectiveness of the proposed method, simulations are carried out based on experimental data. The results show that the control system could accurately track the target trajectory, of which the robot is highly similar to the human.

With the trend of supply chain globalisation, competition among enterprises is becoming more intense. Enterprises urgently need to improve their core competitiveness, and the enhancement of the competencies can depend on technologies services and the quality of suppliers. Since external factors are less controllable, this study starts with the quality of suppliers and proposes a supplier evaluation method that combines particle swarm optimisation with neural network algorithm to maximise the interests of enterprises. The particle swarm algorithm to lock the approximate location of the global optimum is first employed. Based on this, we establish an evaluation model of suppliers to train for the minimum errors between the desired and predicted values by constructing a back propagation (BP) neural network. Finally, the output results of the proposed method is compared with the BP neural network without the particle swarms optimisation. The proposed model is less empirically sensitive to the initialisation and can quickly converge to the local optimums, which overcomes the shortage of traditional neural networks and is more applicable to supplier evaluation.

With the wide application of big data and artificial intelligence technology in road traffic, road planning makes logistics and transportation services more efficient. For the supplier's transportation service problem, a multi-supplier collaborative transportation strategy is designed in this paper. First, we establish a model to minimize the transportation cost, then we simulate a path diagram and calculate the optimal transportation paths of suppliers based on Dijkstra's algorithm. In addition, we obtain multiple alternative paths with K-shortest path algorithm. Finally, simulations of collaborative transportation for suppliers are performed in three scenarios and the results are used to prove the effectiveness of the proposed collaborative transportation strategy. This strategy not only can strengthen the synergistic cooperation among suppliers, but also cultivate the potential customer. Furthermore, it also could improve the flexibility of the supply chain, maximize the overall efficiency and provide a new solution for the development of logistics and transportation services.

With the rapid development of China's economy, enterprises need to plan their logistics transportation routes reasonably in advance. This will make the transportation service more efficient. For the supplier's transportation service problem, an analysis method of critical path nodes is provided and a multi‐supplier collaborative transportation strategy is designed in this article. First, a model for minimising the transportation cost was established, then a path diagram was simulated and the optimal and alternative transportation paths of suppliers based on the k‐shortest path algorithm were calculated. In addition, path node availability during COVID‐19 is used as a research context in this article. A multi‐stage path analysis method was provided by discussing different cases of critical path nodes, which can make a reasonable selection of paths in a timely and effective manner. Finally, simulations of collaborative transportation for suppliers were performed in three scenarios and the results verified the effectiveness of the collaborative transportation strategy. The proposed collaborative transportation strategy of suppliers not only strengthened the synergistic cooperation among suppliers, but also cultivated the potential customer for suppliers in this article. Furthermore, the strategy could improve the flexibility of the supply chain, maximise the overall efficiency and also provide a new solution for the development of logistics and transportation services.

Abstract. Classroom atmosphere plays an important role in second language learning. Although there are many factors that can affect the classroom atmosphere, such as students' learning motivation, and etc., college English teachers also play an important role, especially teachers' verbal language (teacher talk) in class, which is still the main way for teachers and students to interact with each other in class. Based on the great success of excellent TV talk shows in the atmosphere and similarities between English class and TV talk shows, in this paper, it is explored by specifying on the good qualities of TV talk show hosts' words which are worthwhile for college English teachers to learn from. On the basis of this, some suggestions as to refine college English teachers' words to improve class atmosphere were put forward, thus improving classroom qualities and foreign language acquisition.