Hang Su

Since the 1990s, the essential function of teacher trainers in academic courses has gradually attained more attention from scholars. Also, the teacher trainers’ professional development has acquired worldwide attraction following the concept that teacher trainers are deeply liable for educator education quality. The present mini-review of literature indicates that while teacher trainers have several complicated functions, they obtain the least preparation or opportunities for professional development to perform such functions. Consequently, they require getting the related knowledge and skills after accepting the role of teacher trainers. Besides numerous aspects affecting teacher trainers’ professional development, teaching skills, and knowledge have important functions that are at the center of attention in this mini-review of literature. In brief, several implications are presented for the instructional addressees.

This paper presents a local grammar based diachronic investigation of apology in spoken British English, aiming to offer an alternative approach for diachronic speech act analysis and to further explore what the changing patterns of apology would suggest about the social-cultural changes happened and/or happening in the British society. The paper shows that the proposed local grammar approach can contribute to a more delicate and finer-grained speech act annotation scheme, which in turn facilitates a more reliable quantification of speech act realisations across contexts or time. The subsequent investigation shows that apologies in spoken British English are becoming more formulaic and less explicit, which suggests that either social distance has been reduced or that Britain might have become an even more stratified society.

Previous studies have shown that the motion intention recognition for lower limb prosthesis mainly focused on the identification of performed gait. However, the bionic prosthesis needs to know the next movement at the beginning of a new gait, especially in complex operation environments. In this paper, an upcoming locomotion prediction scheme via multilevel classifier fusion was proposed for the complex operation. At first, two motion states, including steady state and transient state, were defined. Steady-state recognition was backtracking of a completed gait, which would be used as prior knowledge of motion prediction. In steady-state recognition, surface electromyographic and inertial sensors were fused to improve recognition accuracy; five typical locomotion modes were recognized by random forest classifier with over 97.8% accuracy. The transient state was defined as an observation period at the initial stage of upcoming movement, in which only the sEMG signal was recorded due to the limitation of sliding window length. LightGBM classifier was validated to outperform other methods in the accuracy and prediction time of transient-state recognition. Finally, a simplified HMM model based on prior knowledge and observation result was constructed to predict upcoming locomotion. The results indicated that the locomotion prediction was over 91% accuracy. The proposed scheme implements the locomotion prediction at the initial stage of each gait and provides critical information for the gait control of lower limb prosthesis.