The doctrine of the mean reflecting Confucian wisdom is an impartial, not extreme attitude and code of conduct, pursuing a mode characterized by stable, coordinated, and sustainable development. The doctrine of the mean emphasizes that people should “be kind to nature”. It attaches great importance to the building of a society in harmony with nature. Therefore, it has great enlightenment on the relationship between man and nature.

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.

The rise of digital communication technologies has significantly changed how people participate in social protests. Digital platforms—such as social media—have enabled individuals to organize and mobilize protests on a global scale. As a result, there has been a growing interest in understanding the role of digital communication in social protests. This manuscript provides a comprehensive bibliometric analysis of the evolution of research on digital communication and social protests from 2008 to 2022. The study employs bibliometric methodology to analyze a sample of 260 research articles extracted from the SCOPUS core collection. The findings indicate a significant increase in scholarly investigations about digital communication and its role in social protest movements during the past decade. The number of publications on this topic has increased significantly since 2012—peaking in 2022—indicating a heightened interest following COVID-19. The United States, United Kingdom, and Spain are the leading countries in publication output on this topic. The analysis underlines scholars employing a range of theoretical perspectives—including social movement theory, network theory, and media studies—to identify the relationship between digital communication and social protests. Social media platforms—X (Twitter), Facebook, and YouTube—are the most frequently studied and utilized digital communication tools engaged in social protests. The study concludes by identifying emerging topics relating to social movements, political communication, and protest, thereby suggesting gaps and opportunities for future research.

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.

Classical photography, aesthetic beauty, and scientific analysis are related. This article explores composition, light manipulation, and emotion to examine the aesthetic components that characterize classical traditions. Pioneers like Julia Margaret Cameron are revealed from a historical perspective, and Ansel Adams' landscapes are the pinnacle of harmony and majesty. Scientific discoveries illuminate the psychology of authenticity and engagement in the digital age while promoting visual literacy. The timeless influence of artistry in the visual narrative is underscored by classic aesthetics, which connect the past, present, and resound through great works.