Thoughts and Strategies of Talent Training Mode of Music Education Major in Chinese Normal Universities: A Case Study of Art College of Henan University

With an example of music education major in Art College of Henan University, this paper looks into talent training mode of music education in Chinese normal universities, tries to seek contradictions between actual talent demand for realistic society and traditional talent training mode, and explores a talent training mode that meets basic requirements of music education in Chinese normal universities through thoughts and practice

Approaches for Vocal Music Teachers to Guide Students to Form Their Own Singing Styles

In this paper, the author mainly proposed the issue of laying particular emphasis on technique and neglecting characteristic in the present teaching of singing against the phenomenon of “a thousand pieces of the same tune” existing in the field of vocal music performance at present. The author expected to give rise to everyone’s attention by virtue of this paper, so as to put the cultivation of students’ unique singing style as an important position, and propose approaches used to guide students to form their own singing styles.  

Development and characterization of EST-SSRs for Muscidae (Diptera) and their cross-genera transferability

EST-SSR (Expressed Sequence Tag-Simple Sequence Repeat)markers were developed and used to examine genetic diversity of species assemblages of ten genera of Muscidae (Diptera), and specifically the genetic diversity of Phaonia Robineau species, from ten regions in China. 18 EST-SSR markers with high polymorphism and clear bands were screened out from 216 tested markers, with 219 alleles in total (95–280 bp) with an average of 6.1 alleles per locus. In various regions in China such as Tibet, Sichuan, and Yunnan, Muscidae species assemblages exhibited rich genetic diversity, with the polymorphism information content (PIC) values ranging from 0.831 to 0.934, while Hainan region showed a relatively low genetic diversity (PIC = 0.511). Low gene flow (Nm = 0.842) was found among the regions, and the average genetic differentiation coefficient (Fst) was 0.238, indicating a high degree of genetic differentiation among the ten surveyed assemblages. Accordingly, the ten genera of Muscidae exhibited high genetic diversity and genetic differentiation

Assessing the coordinative and coupling development of China’s green economic growth: role of sports economics

The increased concern for green economic growth made many countries shift from simply pursuing economic growth to a green economic growth mode to protect resources and the environment. Against this background, we calculated China’s green GDP and studied the impact of economic growth on environmental quality. For green economic growth, we employed income per capita, its square, information and communication technology, sports industry ecosystem efficiency, and human capital. We applied innovative econometric techniques such as cross-sectional dimensions (CSDs) tests, cross-sectional augmented Dickey-Fuller (CADF), cross-sectional augmented Pesaran and Shin (CIPS) tests, Westerlund co-integration test, generalised method of moments (GMM), and fully modified least squares (FMOLS) estimators. The obtained outcomes showed the inverted U-shaped EKC hypothesis, which validates the green economic growth theme. However, ICT was negatively associated with carbon emissions. Similarly, sports industry eco-system efficiency (SIE) showed an insignificant negative impact on the explained variable. Human capital was inversely associated with carbon emissions. In addition, the moderate role of the SIE significantly reduces carbon emissions. In addition, the D-H panel causality test results showed a significant causal association among the selected variables. Owing to the outcomes, we proposed imperative policy implications for the desired level of green economic growth

Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach

This paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) to improve vehicle energy efficiency at signalized intersections. The vehicle agent is specified by integrating the model-based car-following policy, lane-changing policy, and the RL policy, to ensure safe operation of a CV. Subsequently, a Markov Decision Process (MDP) is formulated, which enables the vehicle to perform longitudinal control and lateral decisions, jointly optimizing the car-following and lane-changing behaviors of the CVs in the vicinity of intersections. Then, the hybrid action space is parameterized as a hierarchical structure and thereby trains the agents with two-dimensional motion patterns in a dynamic traffic environment. Finally, our proposed methods are evaluated in SUMO software from both a single-vehicle-based perspective and a flow-based perspective. The results show that our strategy can significantly reduce energy consumption by learning proper action schemes without any interruption of other human-driven vehicles (HDVs)