Since the reform and opening up, China has continuously pushed forward the administrative system reform, adapted to the national conditions and the requirements of the times, and achieved fruitful results. Generally speaking, the successive administrative system reforms have focused on the government and the adjustment of the dynamic relationship between government-market-society. Due to the special characteristics of local foreign affairs departments in the administrative system, the successive reforms have provided less guidance to them, and related research is also relatively lacking. However, from a practical point of view, local foreign affairs offices have long followed the pace of administrative system reform and carried out a series of adjustments and optimizations. As an important element of administrative system reform, the functional transformation of local foreign affairs offices has been continuously promoted along with institutional reform. This research, which is mainly based on talks and supplemented by document comparisons, aims to study the development results and experiences of the Foreign Affairs Office of Shaanxi Province in the context of administrative system reform, and tries to provide a case study for the administrative system reform of local foreign affairs departments.

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 the process of teaching and learning at any stage, the important role of interest guidance cannot be ignored. Especially in college mathematics teaching, mathematical knowledge is very complex and abstract, and most students are unable to effectively understand and master it during the learning process. So it is even more important to fully stimulate students' interest in learning. This article analyzes the significance and current situation of stimulating students' learning interest in university mathematics teaching, and conducts effective strategy analysis. In order to effectively awaken students' desire for knowledge, guide students to change from passive learning to active learning, so that students can continue to grow and progress in this process.