Professional identity among faculty members in private higher education institutions plays a vital role in shaping the quality and sustainability of these institutions. This research aims to investigate the factors influencing the professional identity of teachers in Chengdu's private higher education institutions. The study employs a theoretical framework centered on "identification" with behavior intention, behavior attitude, and sense of belonging as fundamental dimensions. Data were collected through questionnaire surveys and analyzed using SPSS 23.0. The study hypothesizes that behavior intention, behavior attitude, and sense of belonging have a significant positive impact on professional identity among faculty members. Additionally, behavior attitude, subjective norms, and perceived behavioral control are expected to have a significant positive influence on behavior intention, and subjective norms and perceived usefulness may positively affect sense of belonging. The results are expected to provide valuable insights for enhancing the professional satisfaction and educational quality of faculty in private higher education institutions.

Nowadays, the scale of graduate education in our country has been growing, but the quality of graduate education has not been improved. Therefore, how to effectively improve the quality of postgraduate education has become the most concerned issue in the academic circle and universities, which directly highlights that the internal guarantee mechanism of postgraduate students to improve the quality of postgraduate education has become the focus of academic research, in which tutors are the main influencing factors of postgraduate education quality. The tutor plays a positive and dominant role in stimulating, demonstrating, modeling, guiding and infecting the postgraduate's behavior. This paper analyzes the existing problems in exerting the role of postgraduate tutors, and from the problems, puts forward the countermeasures and suggestions to exert and mobilize the initiative of tutors.

In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.

In this study, the authors propose a method that combines CNN and LSTM networks to recognize facial expressions. To handle illumination changes and preserve edge information in the image, the method uses two different preprocessing techniques. The preprocessed image is then fed into two independent CNN layers for feature extraction. The extracted features are then fused with an LSTM layer to capture the temporal dynamics of facial expressions. To evaluate the method's performance, the authors use the FER2013 dataset, which contains over 35,000 facial images with seven different expressions. To ensure a balanced distribution of the expressions in the training and testing sets, a mixing matrix is generated. The models in FER on the FER2013 dataset with an accuracy of 73.72%. The use of Focal loss, a variant of cross-entropy loss, improves the model's performance, especially in handling class imbalance. Overall, the proposed method demonstrates strong generalization ability and robustness to variations in illumination and facial expressions. It has the potential to be applied in various real-world applications such as emotion recognition in virtual assistants, driver monitoring systems, and mental health diagnosis.

In recent years, science and technology have continued to develop and progress, and the level of teaching digitalization has improved. Music education occupies an important position in many universities, and it is necessary and feasible to use digital technology to explore its impact in basic education. The digitalization and technology of music education in colleges and universities have had a positive impact on basic education, such as balancing educational resources, updating education models, optimizing teaching platforms, and practicing student-oriented concepts. In order to maximize the role of digital technology, it can be innovated and applied from sight-singing ear training teaching, orchestration course teaching, and polyphonic course teaching.

As one of the ways of the double reduction policy, the Family Education Promotion Law not only urges state organs and schools to fulfill their obligations, but also contributes to the growth of children, the shaping of family traditions and the vigorous development of society.However families who still believe that the traditional concept of beating still exists and mostly in rural areas, families with advanced concepts believe that children should not take beating and scolding, but conform to their own characteristics and using a scientific way of education and training.

Reading comprehension ability, as a key skill that needs to be developed in English teaching, has attracted high attention from teachers and students in universities. This is not only due to its relatively large proportion in English exams, but also due to the entering of the information age, people need to obtain information from the text through extensive reading and gain a profound understanding of the content of the article. Therefore, in the process of guiding students to learn English knowledge, teachers must take cultivating students' reading comprehension ability as the central link of teaching activities. Through exploring and researching it in teaching, students can improve their reading comprehension level and enable them to have a deeper understanding of the profound connotations to be expressed in future English texts.

This paper investigates and studies the quality of life of primary school students, and the results show that the quality of life of primary school students in Chongchuan District of Nantong is generally at the upper middle level, and it shows a downward trend with age. There were significant differences between 8-year-old boys and girls in “teacher-student relationship”, “learning ability and attitude”, “self-concept relationship”, “peer relationship” and “homework attitude”, and girls were better than boys. There were significant differences between 10-year-old boys and girls in the scores of “teacher-student relationship” and “self-satisfaction relationship”, and girls were better than boys; There were significant differences between 11-year-old boys and girls in the two factors of “activity opportunity” and “athletic ability”, and boys were better than girls. There was no difference in the remaining ages by factor. Improving the quality of life of primary school students requires the active cooperation of schools, teachers and parents, as well as special attention to the differences between boys and girls aged 8, 10 and 11.