In the new era, an important component of China’s social governance system construction is to strengthen and innovate social governance to improve the ability and level of social governance in China. To ensure the long-term stability of the country and the well-being of the vast majority of the people, it is necessary to be adept at strengthening social governance, continuously improve and improve the governance system that is suitable for the development of modern society with scientific thinking methods, and enhance the level and capacity of governance in China. Based on this, this paper discusses how to promote the innovation of social governance in the digital age, and proposes innovative ideas on the model of social organization governance under the guidance of <Economic Diversification Plan for Macao SAR (2024–2028)>.

Bali is the most famous tourist destination in the world, and this popularity has led to a significant rise in the island’s economy. The rise in income has also driven an increase in demand for infrastructure. Moreover, the Bali regional competitiveness index, in the infrastructure pillar, shows a lower figure compared to the national level. So that the Bali Provincial Government focuses on building an infrastructure strategy. This research uses the Input-Output Table (IOT) model, namely the 2016 Bali Province IOT which will be released in 2021. This analysis was chosen because IOT assumes that one sector can be an input for other sectors, in terms of this this is the construction sector. With investment in strategic and monumental infrastructure marking the New Era of Bali, it will result in additional Gross Regional Domestic Product (GRDP) of IDR 18.7 trillion, or in other words Bali’s GRDP will increase by 9.71% from the condition of no investment. This shows that infrastructure development is able to boost Bali’s economy. Further research is needed to be able to qualitatively analyze development infrastructure strategies in Bali. Remembering that a qualitative approach is also important to be able to analyze in depth.

In modern society, English, as an important language, is an indispensable tool for people to communicate and exchange. However, learning English is not limited to knowledge points, grammar and other aspects. With the development and progress of the times and the rapid improvement of the level of science and technology, the problem of how to cultivate students' interests has become increasingly prominent. Interest is the best teacher to learn, and it is also the most effective, direct and lasting way for students to learn English well and improve their level and ability. Cultivating good teaching habits can help us master knowledge and skills better. Starting from the importance of students' interest in learning, this paper discusses how to stimulate students' interest in learning and find the correct teaching methods in order to help students have a strong interest in English learning in class and help them acquire knowledge actively.

For a long time, kindergarten literature reading course is often a mere formality, preschool children's reading invalid, random phenomenon. In order to improve preschool children's reading interest and reading comprehension ability, teachers should start from the core quality and deconstruct the characteristics of children's literature. Make use of multiple resources to optimize literary reading materials; Integrate contents in various fields and implement rich curriculum activities; Construct performance evaluation system and form reading evaluation model.

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.

Under the background of the development of the network information age, the current Internet industry has obtained more development opportunities, but it has also brought corresponding challenges in the process of wide application. In the development and construction of modernization, society pays more attention to the supervision and determination of the characteristics of online public opinion. From the perspective of the current characteristics of network public opinion, because social information is more extensive and involves many fields, network public opinion has a high degree of complexity and diffusion. Therefore, it is necessary to strengthen the analysis and application of relevant data mining systems in order to achieve efficient management of network public opinion. The key to the disadvantage of the traditional excavation of public opinion communication characteristics lies in the lag of the excavation process, and it is difficult to deal with malignant public opinion in a timely and effective manner. Therefore, in order to truly solve the lagging problem of public opinion data dissemination feature mining technology, it is necessary to strengthen the application of artificial intelligence technology in it.