This research aims to examine the structural relationships between the dimensions of workation attachment, workationer power, the dimensions of workation relationship quality, and workation intention. It demonstrates that the proposed model aligns well with the collected data based on a convenience sample comprising 494 workationers in Bangkok using structural equation modeling. The analysis outcomes contribute to the tourism marketing theory by providing additional insights into the dimensions of workation attachment, workationer power, the dimensions of workation relationship quality, and workation intention. The findings from this study can aid workation managers in formulating and executing market-oriented service strategies to enhance the dimensions of workation attachment, workationer power, and workation relationship quality and foster workation intention.

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.

This paper aims to explore how to build a sustainable peace and development model for China’s peacekeeping efforts through the application of data-driven methods from UN Global Pulse. UN Global Pulse is a United Nations agency dedicated to using big data and artificial intelligence technologies to address global challenges. In this paper, we will introduce the working principles of UN Global Pulse and its application in the fields of peacekeeping and development. Then, we will discuss the current situation of China’s participation in peacekeeping operations and how data-driven methods can help China play a greater role in peacekeeping tasks. Finally, we will propose a sustainable peace and development model that combines data-driven methods with the advantages of China’s peacekeeping efforts to achieve long-term peace and development goals.

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.