The Ling Six Sounds" are a range of speech sounds encompassing the speech frequencies that are widely used clinically to verify the effectiveness of hearing aid fitting in children. This study focused on the spectral features of the six sounds in Standard Chinese. We examined the frequency range of /m, u, a, i, ʂ, s/ as well as three consonants in syllables, i.e., /m(o)/, /ʂ(ʅ)/, and /s(ɿ)/. We presented the frequency distribution of these sounds. Based on this, we further proposed guidelines to improve "the Ling Six-Sound Test" regarding tones in Standard Chinese. We also suggested further studies in other dialects/languages spoken in China with regard to their phonological specifics.

Teachers are a very important part of university education. They have the responsibility of teaching and educating people, and it is also their unshakable responsibility to train all-round talents for the country. If we want to improve students’ quality, we must improve teachers’ teaching quality and pay attention to the research of teachers’ teaching ability. This paper analyzes the connotation of artificial intelligence-assisted instruction. Then, Bayesian active learning modeling is used. This paper mainly adopts the way of questionnaire and empirical research methods and launches a basic investigation on the teaching ability of university teachers. Through investigation, the following problems are summarized: (1) insufficient self-knowledge reserve and weak teaching theoretical foundation and (2) inaccurate orientation of teaching objectives and single teaching methods. Schools need to enrich training methods, establish multiple effective mechanisms for evaluation, meet the basic requirements of each teacher, and play the role of inspiring teachers. As for teachers, they need to have a good attitude, be full of interest in teaching and educating people, have a strong sense of responsibility, and constantly improve themselves and improve themselves.

In view of a complex multi-factor interaction relationship and high uncertainty of a battlefield environment in the anti-missile troop deployment, this paper analyzes the relationships between the defending stronghold, weapon system, incoming target, and ballistic missile. In addition, a double nested optimization architecture is designed by combining deep learning hierarchy concept and hierarchical dimensionality reduction processing. Moreover, a deployment model based on the double nested optimization architecture is constructed with the interception arc length as an optimization goal and based on the basic deployment model, kill zone model, and cover zone model. Further, by combining the target full coverage adjustment criterion and depth-first search, a deep Kuhn–Munkres algorithm is proposed. The model is validated by simulations of typical scenes. The results verify the rationality and feasibility of the proposed model, high adaptability of the proposed algorithm. The research of this paper has important enlightenment and reference function for solving the force deployment optimization problems in uncertain battlefield environment.

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