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.

Local community members play a critical role in the success of conservation projects, which in turn have the potential to influence the perceptions of local people. Relationships matter when it comes to sustainable long-term conservation and community well-being. The study aims to establish the relationship between local communities and wildlife conservation organizations in the context of Phinda Private Game Reserve and the Mnqobokazi community, located in South Africa. Data was collected using the qualitative methods of interviews and focus group discussions. The findings show that a symbiotic relationship between conservation organizations and local community members is critical in conserving the environment. The research indicates that both participation and benefits result in improved perceptions towards the protected area and a strong positive relationship. The accrual of benefits also appears to result in pro-environmental consciousness amongst community members. Several existing studies examine participation or benefit-sharing in community-based tourism in developing nations. However, less is known about the relationships between local communities and conservation organizations and the effect of participation and beneficiation on these relationships. This research narrows this gap in the body of knowledge by qualitatively examining a single case study. The findings add value to global collaborative efforts aimed at achieving positive relationships between communities and conservation management.

Taking domestic single-player game brands as the research object, this paper discusses the value and strategy of single-player game brands expanding IP operation. It is found that single-player game brands expanding IP operation can improve brand awareness and influence, increase player stickiness and loyalty, and extend product life cycle and market vitality. In order to help single-player game brands expand IP operations, this paper puts forward four suggestions, such as creating core stories, hoping to provide some reference and inspiration for the development of domestic single-player game brands.

The consumption of dietary supplements among the elderly is on the rise. Despite the potential benefits, a comprehensive understanding of the decision-making processes leading to the consumption is lacking. This study explores the conditions influencing the decision-making and behavioral patterns of older adults related to dietary supplement consumption. Using a qualitative approach, in-depth interviews were conducted with 21 elderly participants from a seniors’ club in Bangkok, Thailand, who had consistently consumed dietary supplements for at least one year. The behavior was classified into five primary categories: enduring use of identical dietary supplements, insufficient regard for health compatibility, replacing medications with supplements, not verifying before consumption, and opting for supplements over medical treatments. These patterns are aligned with the core constructs of the Theory of Planned Behavior (attitude, subjective norm, and perceived behavioral control). Many individuals perceive supplements as pivotal health investments, while others view them as a direct route to robust health. Trusted advice from friends and television significantly influence their choices, with a prevailing sentiment that dietary supplements are generally safe. The high price tag on supplements is often associated with superior quality. The findings highlight the multifaceted nature of dietary supplement consumption decisions among the Thai elderly, suggesting the need for interventions to promote safer and more informed choices.

The Malaysian dilemma presents a complex challenge in the wake of the COVID-19 pandemic, requiring a comprehensive statistical analysis for the formulation of a sustainable economic framework. This study delves into the multifaceted aspects of reconstructing Malaysia’s economy post-COVID-19, employing a data-driven approach to navigate the intricacies of the nation’s economic landscape. The research focuses on key statistical indicators, including GDP growth, unemployment rates, and inflation, to assess the immediate and long-term impacts of the pandemic. Additionally, it examines the effectiveness of government interventions and stimulus packages in mitigating economic downturns and fostering recovery. A comparative analysis with pre-pandemic data provides valuable insights into the extent of economic resilience and identifies sectors that require targeted support for sustained growth. Furthermore, the study explores the role of technology and digital transformation in building a resilient economy, considering the accelerated shift towards remote work and digital transactions during the pandemic. The analysis incorporates data on technological adoption rates, digital infrastructure development, and innovation ecosystems to gauge their contributions to economic sustainability. Addressing the Malaysian Dilemma also involves an examination of social and environmental dimensions. The study investigates the impact of economic policies on income distribution, social equity, and environmental sustainability, aiming to achieve sustainable economic growth. The study contributes a nuanced analysis to guide policymakers and stakeholders in constructing a sustainable post-COVID-19 economy in Malaysia.