This article reports on a study that examined the effects of integrating corpus and contextualized lexicogrammar in foreign and second language teaching. The study was conducted in English as a foreign language (EFL) and English as a second language (ESL) courses at 1 Chinese university and 2 U.S. universities, involving 244 participants (236 EFL/ESL students and 8 instructors). A variety of data was collected, including students' corpus search projects and reflection papers, teachers' lesson plans and teaching journals, and a poststudy assessment survey. A close analysis of the data reveals several positive effects of the approach, such as improved command of lexicogrammar, increased critical understanding of grammar, and enhanced discovery learning skills. It also reveals some challenges of corpus-based lexicogrammar learning, including the daunting difficulty many students feel in corpus analysis. The study also identifies some variables influencing learners' experience in using the approach, such as course content, student learning styles, and learning settings. Implications for pedagogy and further research are also discussed.WITH NEW UNDERSTANDINGS ABOUT grammar arising from linguistics and applied linguistics and with the rapid advancement of educational technology, novel theories and teaching practices for grammar instruction have been proposed, including teaching grammar in discourse contexts, approaching grammar from a lexicogrammatical perspective, and using corpus data-driven learning. So far, however, there has been little empirical research on the effectiveness of these new theories and practices.

The development of a physiologically plausible computational model of a neural controller that can realize a human-like biped stance is important for a large number of potential applications, such as assisting device development and designing robotic control systems. In this paper, we develop a computational model of a neural controller that can maintain a musculoskeletal model in a standing position, while incorporating a 120-ms neurological time delay. Unlike previous studies that have used an inverted pendulum model, a musculoskeletal model with seven joints and 70 muscular-tendon actuators is adopted to represent the human anatomy. Our proposed neural controller is composed of both feed-forward and feedback controls. The feed-forward control corresponds to the constant activation input necessary for the musculoskeletal model to maintain a standing posture. This compensates for gravity and regulates stiffness. The developed neural controller model can replicate two salient features of the human biped stance: (1) physiologically plausible muscle activations for quiet standing; and (2) selection of a low active stiffness for low energy consumption.

The human body is a complex system driven by hundreds of muscles, and its control mechanisms are not sufficiently understood. To understand the mechanisms of human postural control, neural controller models have been proposed by different research groups, including our feed-forward and feedback control model. However, these models have been evaluated under forward and backward perturbations, at most. Because a human body experiences perturbations from many different directions in daily life, neural controller models should be evaluated in response to multidirectional perturbations, including in the forward/backward, lateral, and diagonal directions. The objective of this study was to investigate the validity of an NC model with FF and FB control under multidirectional perturbations. We developed a musculoskeletal model with 70 muscles and 15 degrees of freedom of joints, positioned it in a standing posture by using the neural controller model, and translated its support surface in multiple directions as perturbations. We successfully determined the parameters of the neural controller model required to maintain the stance of the musculoskeletal model for each perturbation direction. The trends in muscle response magnitudes and the magnitude of passive ankle stiffness were consistent with the results of experimental studies. We conclude that the neural controller model can adapt to multidirectional perturbations by generating suitable muscle activations. We anticipate that the neural controller model could be applied to the study of the control mechanisms of patients with torso tilt and diagnosis of the change in control mechanisms from patients’ behaviors.

No abstract

The reasonable control of the grate cooler is the key factor to ensure the heat exchange and cement clinker quality during the clinker cooling process. In this paper, the cement grate cooler pressure of the grate cooler is taken as the research object and a cement grate cooler pressure prediction model is proposed based on the analysis of the current status of the automatic control of the grate cooler. This model uses a multi-model fusion neural network algorithm that combines a BP neural network, a support vector machine and classification and regression trees with a neural network structure. Furthermore, the multimodel fusion quality characteristics are proposed, and the root mean square error and Pearson linear correlation coefficient of the multi-model fusion quality characteristics are used as the evaluation indicators for the prediction results of the multi-model fusion neural network. After the analysis of the cooling process of the cement clinker, we select seven input variables, and then complete the data preprocessing and model parameter selection. Finally, we predict the cement grate cooler pressure using a multi-model fusion neural network, a BP neural network, a support vector machine and classification and regression trees with three training sets to test sets ratios. Through the comparison of the root mean square error and the Pearson linear correlation coefficient evaluation indicators and their change trends, as well as the display and analysis of the final modelling results, it is found that the multi-model fusion neural network algorithm can greatly improve the accuracy of the prediction of the grate pressure, and at the same time it has good practicality for the accurate prediction of the cement grate cooler pressure in the industry.