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.

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.

With the globalization of social and economic development, the culture, economy, science and technology and materials of all countries in the world are communicating to varying degrees. The basic tool of communication is language. Therefore, language translation plays an important role in this process. Learning English translation is of great significance to the development and construction of our country. The establishment of English translation major in major universities is the base for cultivating English translation talents in our country. It is also the main place to improve students' English translation ability and practice. The text will focus on the existing problems and teaching practice application strategies of practical English in higher vocational education, so as to promote the development and use of English translation and improve the quality of teaching in our country.

The COVID-19 pandemic occasioned significant changes in many aspects of human life. The education system is one of the most impacted sectors during the pandemic. With the contagious nature of the disease, governments around the world encouraged social distancing between individuals to prevent the spread of the virus. This led to the shutdown of many academic institutions, to avoid mass gatherings and overcrowded places. Developed and developing countries either postponed their academic activities or used digital technologies to reach learners remotely. The study examined the benefits of online learning during the COVID-19 pandemic. The participants for the study consist of 5 lecturers and 30 students from the ML Sultan Campus of the Durban University of Technology, South Africa. Data was collected using open-ended interviews. Content analysis was applied to analyze the data collected. Data was collected until it was saturated. Different ways were implemented to make online learning and teaching successful. The findings identified that the benefits of online learning were that it promotes independent learning, flexible learning adaptability and others.