The purpose of the study is to create proposals and recommendations to improve the system evaluating the quality of governance and efficient use of budget funds in order to improve public welfare and sustainable development. The research methodology included application of statistical methods to review scientific articles, legislative acts and other documents, study models for evaluating the quality of governance and efficient use of budget funds. Mathematical modeling and forecasting methods were also used to assess aspects of governance and predict the results when changes are made, including building a trend model and determining the forecast values of accrued taxes and mandatory payments for 2024–2026. The conclusions highlight there is a positive correlation between the accrued taxes and mandatory payments to the budget of the Republic of Kazakhstan, and an economic growth and changes in tax legislation. The key factors influencing the quality of governance and efficient use of budget funds were identified. Recommendations were developed to improve the quality assessment system and governance of budget funds in order to increase efficiency and responsibility in financial management. The results of the study can be used by public administration bodies and financial institutions to optimize the governance of budget funds.

Our study is based on the premise that every crisis has historical precedents and antecedents. First, we analyze past crises, beginning with the experiences of the Dutch tulip bulb crisis. Then, we review major cataclysms, such as World War I, the Spanish flu crisis, the Great Depression of 1929–1933, World War II and the subsequent transition to socialism, the 1973 oil shock, the regime change of 1989, and the 2008–2009 global financial crisis from both general and corporate perspectives. Throughout history, periods of crisis have alternated with phases of development. During times of crisis, people’s behavior changes as they search for solutions and support. This pattern is evident across all levels of economic activity, where governments, organizations, and individuals do their utmost to achieve a quick recovery. Sometimes, they look to external aid, forgetting that lessons from the past may provide guidance for crisis management. Without claiming to be exhaustive, we have identified points worthy of consideration. Our goal is to offer guidance for business organizations, complemented by thoughts addressed to individuals and governments alike. Organizations must pay attention to the first signs of crises and either proceed according to a pre-developed fitting strategy or revise it according to specific circumstances. They cannot avoid the consequences, but they can mitigate the negative effects.

The sustainable development of Madeira Island necessitates the implementation of more precise and targeted planning strategies to address its regional challenges. Given the urgency of this issue within the context of sustainability, planning approaches must be grounded in and reinforced by a comprehensive array of thematic studies to fully grasp the complexities involved. This research leverages Geographic Information Systems (GIS) to analyze land use and occupancy patterns and their evolution within the municipality of Machico on Madeira Island. The study provides a nuanced perspective on the urban structure’s stagnation in the region, while concurrently highlighting the dynamic shifts in agricultural practices. Furthermore, it elucidates the transformation of predominant native vegetation within the municipality from 1990 to 2018. Notably, the research underscores the alarming decline in native vegetation due to anthropogenic activities, emphasizing the need for more rigorous monitoring by regional authorities to safeguard and preserve these valuable landscapes, habitats, and ecosystems.

The article addresses the issue of educational development policy in Ukraine: the main trends and ways, means, technologies of their implementation. It has been observed that educational policy is developing and changing under the influence of such factors as Russia’s military actions against our country, European integration and globalisation. It has been taken into account that globalisation trends in the world integration, according to which globalisation processes should be reflected not only in the foreign economic, political or technological spheres, but also, as a consequence, in the development of technologies for training future teachers. Integration of digital technologies in the educational process is one of the key tendencies in the modern educational policy in Ukraine. The characteristics of the most used technologies of augmented reality in the modern school of Ukraine have been outlined. The algorithm for displaying generalized information about a particular application was proposed, namely: payment, accessibility, language, system requirements; learning opportunities; practical value; website; video about the application. The model of the formation of future teachers’ skills to use augmented reality technologies in the process of natural sciences studying has been proposed. We consider it as a component of a holistic system of future teachers’ professional training. The conceptual basis for the development of the model is a multi-subject educational paradigm, which is considered to be open, self-developing and self-organizing, causing a fundamental change in the behavior and relationships of the educational process participants. The proposed model is implemented in the authors’ methodological system, which ensures the interconnected activities of all participants in the educational process. Its systemic factor is the goal of improving the quality of the future natural sciences teachers’ professional training by developing their skills in using AR technology. The end result is an increase in the level of future natural sciences teachers’ readiness to use AR technology in their professional activities.

Raising public awareness of maritime risk and disseminating information about disaster prevention and reduction are the most frequent ways that the government incorporates citizens in marine disaster risk management (DRM). However, these measures are deemed to be insufficient to drive the participation rate. This study aims to understand the participation trend of citizens in marine DRM. On the basis of the theory of citizen participation’s ladder, public participation within marine DRM is categorized into non-participation, tokenistic participation, and substantive participation. Using organization theory, the government’s strategies for encouraging participation are classified into common approach (raising awareness), structural approach (innovating instruments), and cultural approach (developing citizenship). Considering the vignette experiment of 403 citizens in a coastal city of China that has historically been subject to marine disasters, it was found that effectiveness of the strategies, from highest to lowest, are citizenship development, risk education, and instruments innovation. At the individual level, psychological characteristics such as trust in the government, past disaster experience, and knowledge of marine DRM did not significantly influence citizens’ participation preferences. At the government level, even when citizens are informed about new participatory mechanisms and tools, they still tend to be unwilling to share responsibilities. However, self-efficacy and understanding the beneficial outcomes of their participation in marine (DRM) can positively impact the willingness to participate. The results show that to encourage public participation substantively in the marine DRM, it is important to cultivate a sense of civic duty and enhance citizens’ sense of ownership, fostering a closer and more equitable partnership between the state and society.

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.