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.

Tennis, as an important project of sports, has an obvious role in cultivating students' physical quality, observation ability and reaction speed. Under the background that the country attaches great importance to students' physical literacy, the traditional single and passive teaching has been unable to meet the practical needs, so it is necessary to strengthen the innovation of teaching methods. With the recognition of many teachers, the teaching method of "combining competition and practice" has been continuously promoted. The paper takes the combination of competition and practice as the teaching mode, and studies the teaching of college tennis sports.

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 a result of China's evolving higher education landscape, private universities have emerged as significant players, fostering democratization and fulfilling key roles. However, these institutions face distinct challenges shaped by legal, societal, and internal factors. In the knowledge-driven economy, employee satisfaction is crucial for success. Understanding pivotal factors and conducting satisfaction surveys are essential for effective management and talent retention. This study focuses on Chengdu's private university educators, analyzing how factors like belongingness, self-actualization, and rewards influence job satisfaction. Through surveys, data analysis, and literature review, this study refines its findings and uncovers underlying causes. The study offers actionable insights for educators and institutions, aimed at enhancing job satisfaction.