A Case Study on the Role Models in Chinese Senior High School English Textbooks—The Case of the Textbooks Published by BNUP in 2019

Moral education has always been an important mission in Chinese education. English textbooks, as one of the vital elements in English teaching, are the main vehicle for transmitting English disciplinary knowledge, and the essential medium for teachers’ teaching and students’ learning. In fact, the values are often not directly presented in textbooks but are “hidden” in vehicles such as figures and storylines constructed by complex multimodal symbols (Feng, 2015). Therefore, it is essential to explore the images of figures and their values in Chinese English textbooks. This study takes one version of Chinese senior high school English textbooks published by BNUP in 2019 as the research objects. Based on the data collection and thorough analysis, the results show that Chinese senior high school English textbooks published by BNUP emphasize the moral education of students, and the selection of role models is relatively reasonable. Nevertheless, several shortcomings still exist, such as the unbalanced selection of role models in terms of gender and age

Design, Modeling and Control of a Thermal Management System for Hybrid Electric Vehicles

Hybrid electric vehicle (HEV) technology has evolved in the last two decades to become economically feasible for mass produced automobiles. With the integration of a lithium battery pack and electric motors, HEVs offer a significantly higher fuel efficiency than traditional vehicles that are driven solely by an internal combustion engine. However, the additional HEV components also introduce new challenges for the powertrain thermal management system design. In addition to the common internal combustion engine, the battery pack, the generator(s), as well as the electric motor(s) are now widely applied in the HEVs and have become new heat sources and they also require proper thermal management. Conventional cooling systems have been typically equipped with a belt driven water pump and radiator fan, as well as other mechanical actuators such as the thermostat valve. The operation of these components is generally determined by the engine speed. This open-loop cooling strategy has a low efficiency and suffers the risk of over-cooling the coolant and components within the system. In advanced thermal management systems, the mechanical elements are upgraded by computer controlled actuators including a servo-motor driven pump, variable speed fans, a smart thermostat, and an electric motor driven compressor. These electrified actuators offer the opportunity to improve temperature tracking and reduce parasitic losses. This dissertation investigates a HEV powertrain thermal management system featuring computer controlled cooling system actuators. A suite of mathematical models have been created to describe the thermal behaviour of the HEV powertrain components. Model based controllers were developed for the vehicle\u27s cooling systems including the battery pack, electric motors, and internal combustion engine. Optimal control theory has been applied to determine the ideal battery cooling air temperature and the desired heat removal rate on e-motor cooling surface. A model predictive controller(MPC) was developed to regulate the refrigerant compressor and track the battery cooling air temperature. A series of Lyapunov-based nonlinear controllers have been implemented to regulate the coolant pumps and radiator fans in the cooling systems for the engine and e-motors. Representative numerical results are presented and discussed. Overall, the proposed control strategies have demonstrated the effectiveness in improving both the temperature tracking performance and the cooling system power consumption reduction. The peak temperature error in the selected A123 battery core can be tracked within 0.25 C of the target; a 50% reduction of the vapor compression system energy consumption can be obtained by properly designing the cooling air flow structure. Similarly, the cooling system of HEV electric motors shows that the machine internal peak temperature can be tracked to the target value with a maximum error of 3.9 C and an average error of 0.13 C. A 70% to 81% cooling system energy consumption reduction can be achieved under different driving cycle comparing to classical controller applied to maintain a similar level of hotspot temperature stabilization. The proposed optimal nonlinear controller tracks the engine coolant temperature with an average error of 0.35 C and at least 13% reduction in engine cooling power. Further, a close analysis on the cooling system energy consumption reduction has been conducted with a heat exchanger simulation tool established for cooling system design optimization. This research has developed the basis for the holistic control of HEV powertrain thermal management systems by including a suite of model based nonlinear controllers to simultaneously regulate the cooling actuators for the battery pack, e-motors, and conventional internal combustion engine. Numerical studies has been conducted with a high fidelity HEV model under real driving cycles to demonstrate the advantages of introducing advanced control theory into multi-mode vehicle drive systems

Complete ff-moment convergence for weighted sums of WOD arrays with statistical applications

summary:Complete $f$-moment convergence is much more general than complete convergence and complete moment convergence. In this work, we mainly investigate the complete $f$-moment convergence for weighted sums of widely orthant dependent (WOD, for short) arrays. A general result on Complete $f$-moment convergence is obtained under some suitable conditions, which generalizes the corresponding one in the literature. As an application, we establish the complete consistency for the weighted linear estimator in nonparametric regression models. Finally, some simulations are provided to show the numerical performance of theoretical results based on finite samples

Does regional value chain participation affect global value chain positions? Evidence from China

Does participation in the ASEAN-China regional value chain (RVC) affect China’s manufacturing enterprises’ global value chain (GVC) positions in the context of the establishment of the ASEAN-China Free Trade Area (ACFTA)? In this paper, we discuss the theoretical mechanisms and impacts of RVC participation on GVC positions and use an input-output model to decompose the gross exports of China by different sources and destinations. The model measures China’s manufacturing industries’ total, upstream and downstream participation within the ASEAN-China regional value chain. Using panel data from the OECD for 2005 to 2015, the empirical results show that (1) the participation of China’s manufacturing industries in the RVC is conducive to improvement in their GVC positions, (2) moving to more upstream can indeed promote the GVC positions of enterprises, and (3) in contrast to labour-intensive and capitalintensive manufacturing, knowledge-intensive manufacturing in upstream activities of the RVC contributes the most to GVC positions. It is suggested that China should develop knowledge-oriented industries and move to more upstream of the ASEAN-China RVC to raise manufacturing industries’ positions in the GVC

A Robust Method for Speech Emotion Recognition Based on Infinite Student’s t

Speech emotion classification method, proposed in this paper, is based on Student’s t-mixture model with infinite component number (iSMM) and can directly conduct effective recognition for various kinds of speech emotion samples. Compared with the traditional GMM (Gaussian mixture model), speech emotion model based on Student’s t-mixture can effectively handle speech sample outliers that exist in the emotion feature space. Moreover, t-mixture model could keep robust to atypical emotion test data. In allusion to the high data complexity caused by high-dimensional space and the problem of insufficient training samples, a global latent space is joined to emotion model. Such an approach makes the number of components divided infinite and forms an iSMM emotion model, which can automatically determine the best number of components with lower complexity to complete various kinds of emotion characteristics data classification. Conducted over one spontaneous (FAU Aibo Emotion Corpus) and two acting (DES and EMO-DB) universal speech emotion databases which have high-dimensional feature samples and diversiform data distributions, the iSMM maintains better recognition performance than the comparisons. Thus, the effectiveness and generalization to the high-dimensional data and the outliers are verified. Hereby, the iSMM emotion model is verified as a robust method with the validity and generalization to outliers and high-dimensional emotion characters