Ideological and political education is not only a basic course to cultivate students’ moral quality, but also an important part of modern education outline. The current intelligent electronic technology course should strengthen the gradual integration of curriculum and ideological education. Under the background of the new era, the state pays more attention to education, aiming to integrate the concept of ideological and political education into the classroom to effectively improve the effectiveness of comprehensive education for students. The course of intelligent electronic technology should integrate ideological and political education resources and innovate educational means from teachers to classrooms. This paper analyzes the principle of Integrating ideological and political education into intelligent electronic technology curriculum, and hopes to put forward constructive suggestions on the research and innovation path.

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.

The discourse on advocacy planning involving actors has not explicitly addressed the question of who the actor advocate planner is and how an actor can become an advocate planner. This paper attempts to exploring the actor advocate planner in the context of Regional Splits as, employing social network analysis as a research tool. This research employs an exploratory, mixed-methods approach, predominantly qualitative in nature. The initial phase entailed the investigation and examination of qualitative data through the acquisition of information from interviews with key stakeholders involved in Regional Splits, including communities, non-governmental organizations (NGOs), governmental entities, and political parties. The subsequent phase utilized quantitative techniques derived from the findings of the qualitative analysis, which were then analysis into the Gephi application. The findings indicate that the Regional Splits the Presidium Community represents civil society and political parties serve as crucial advocate planners, facilitating connections between disparate actors and promoting Regional Splits through political parties.

With the continuous deepening of education and teaching reform and the rapid development of information technology, more and more educators have paid attention to the effectiveness of computer teaching in universities, and university teachers have included it in the key topics of educational and teaching activity design. The cooperative teaching model stands out among numerous teaching models with its unique advantages, and has been applied by many teachers in the classroom teaching process, playing an important role in promoting the improvement of teaching efficiency. Teachers of computer science in universities should continue to explore effective teaching methods, accelerate the process of organic integration of cooperative teaching models and classroom teaching content, in order to deepen the understanding and cognition of computer knowledge among more students and promote their comprehensive development. This article will study the application strategies of cooperative teaching mode in computer teaching in universities.

This study addresses the impact of the tourism sector on poverty, poverty depth, and poverty severity in Indonesia, focusing on the micro-level dynamics in the province. Despite numerous tourism destinations, their strategic contribution to regional progress remains underexplored. The motivation stems from the need to comprehend the nuanced relationship between tourism and poverty at both the national and local levels, with specific attention to the untapped potential at the province level in Indonesia. We hypothesize that a higher tourism sector GRDP will be inversely correlated with poverty levels, and the inclusion of a Covid-19 variable will reveal a structural impact on poverty dynamics. Employing a Panel Regression Model, secondary data from the Central Statistics Agency (BPS) spanning 2011–2020 is utilized. A panel data regression equation model, including CEM, FEM, and REM, is employed to analyze the intricate relationship between tourism and poverty. The findings demonstrate a negative correlation between higher tourism sector GRDP and the number of poor people. The Covid-19 variable, considered a structural break, reveals a significant association between increased cases and elevated poverty and severity across Indonesian provinces. This study contributes a micro-level analysis of tourism’s role, emphasizing its impact at the provincial level. The findings underscore the need for strategic initiatives to harness the untapped potential of tourism in alleviating poverty and promoting regional progress.