Ancestral knowledge is essential in the construction of learning to preserve the sense of relevance, transmit and share knowledge according to its cultural context, and maintain a harmonious relationship with nature and sustainability. The objective of this research is to study and analyze the management of ancestral knowledge in the production of the Raicilla to provide elements to rural communities, producers, and facilitators in decision-making to be able to innovate and be more productive, competitive, sustainable, and improve people’s quality of life. The methodological strategy was carried out through Bayesian networks and Fuzzy Logic. To this end, a model was developed to identify and quantify the critical factors that impact optimally managed technology to generate value that translates into innovation and competitive advantages. The evidence shows that the optimal and non-optimal management of knowledge, technology, and innovation management and its factors, through the causality of the variables, permits us to capture the interrelationship more adequately and manage them. The results show that the most relevant factors for adequate management of ancestral knowledge in the Raicilla sector are facilitators, denomination of origin, extraction and fermentation, and government. The proposed model will support these small producers and help them preserve their identity, culture, and customs, contributing greatly to environmental sustainability.

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.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

This paper analyzes the relevance of social accounting information for managing financial institutions, using Banca Transilvania Financial Group (BTFG) as a case study. It explores how social accounting data can enhance decision-making processes within these institutions. Social information from BTFG’s annual integrated reports was used to construct a social balance sheet, and financial data was collected to calculate economic value added (EVA) and social value added (SVA). Research question include: Does social accounting represent a lever for substantiating the managerial decision in financial institutions? Results show that SVA is a valuable indicator for financial institution managers, reflecting the institution’s contributions to social well-being, environmental impact, and community support. Policy implications suggest regulatory bodies should mandate the inclusion of social accounting metrics in financial reporting standards to encourage socially responsible practices, enhance transparency, and incentivize institutions achieving high SVA. This paper contributes to the literature by demonstrating the practical application of social accounting in financial institutions and highlighting the importance of SVA as a managerial tool. It aligns with existing research on integrating corporate social responsibility (CSR) metrics into financial decision-making, enhancing the understanding of combining social and economic indicators for comprehensive performance assessment The abstract covers motivation, methodology, results, policy implications, and contributions to the literature.

Carbonated soft drinks (CSDs) have long been a mainstay of the beverage business but changing consumer tastes and rising health awareness have necessitated a thorough study of the variables impacting consumer choices. This study intends to explore the complex web of customer preferences, purchasing behaviour, and perceptions related to carbonated soft drinks. This research analyses how numerous variables, including gender, affect these preferences and choices via careful examination. The purpose of thepresent research is to determine the perception of consumer influencing customer choice preferences for the consumption of carbonated soft drinks, influence of gender and the role of advertisement in finalizing the choice. It would be helpful to do further research to better understand how these highlighted variables affect purchasing choices, especially gender-based variances. The important influence of gender on consumer behaviour has been acknowledged. For this study, a structured questionnaire was distributed through online social media to individuals of 12–45 years of age from the period of April–May 2023. For analysis of the data collected, SPSS 22.0 was used. The study has confirmed that consumption of Coca-Cola is higher than any other soft drink in almost the entire country. The factors like youthfulness, tradition, status symbol and level of carbonation have different influences on the buying behavior of male and female consumers.