The purpose of this paper is to suggest some ways and mechanisms for creating long-term peace based on sustainable development of the world and the purpose of the work is to develop recommendations aimed at counteracting the emergence of dictatorial regimes that were legitimately established. Five common features of such dictators have been identified, namely: coming to power in a legitimate way, using manipulative technologies, openly declaring their aggressive intentions, gradually implementing their aggressive intentions, creating a military potential with the active participation of developed countries, including those with established democracies. The reasons for the creation of dictatorial regimes are substantiated, namely: the imperfection of electoral legislation, excessive conservatism of legislation, insufficient determination and timeliness of countering the strengthening of dictatorships, “national egoism”, the unscrupulousness of dictators in their foreign and domestic policies. It was determined that in order to actively oppose dictatorial regimes, it is necessary to: improve the system of elections to the highest positions and to the legislative bodies of the state, put a strong barrier against manipulative technologies and fakes, through the improvement and effective application of international legislation with the involvement of artificial intelligence, determine the strategy of relations with dictators in all directions in advance: economic, diplomatic, sports, scientific and technical, etc., establish the scope of relations in direct proportion to the index of democracy in a country with an authoritarian regime and, in order to prevent negative consequences on the economy and social condition of the society of one’s country, determine and carefully regulate import and export activities. It is proposed to start an indicator of the effectiveness of the head of state and an internal truth index of the head of state, as well as measures for moral stimulation of heads of state. As a result of the study, two root causes of threats to the existence of humanity were additionally identified, which directly affect the formation of dictatorial regimes. 1) The emergence on the basis of modern information technologies of a powerful system of manipulative technologies, the use of which leads to the power of future dictators. 2) Belated opposition of the democratic world to the formation of dictatorships. This is expressed in condescension to the initial illegal actions of future dictators, uncontrolled cooperation in the economic, political and humanitarian spheres. Two key mechanisms for achieving sustainable development and long-term peace are proposed.

The research aims to examine the determinants influencing the business commitment toward sustainable goals in Vietnam. To employ a quantitative research approach, we surveyed 208 business leaders in Vietnam to assess their perceptions and actions regarding sustainable goals. We explored the impact of internal enterprise characteristics and external facilitating factors on different dimensions of sustainable goals by using logistic regression models. This paper’s findings reveal that enterprise attributes, corporate leadership traits, and external factors significantly influence sustainable goal engagement. Notably, corporate leaders emerge as pivotal factors, particularly in their willingness to embrace risks and uncertainties. Moreover, this paper’s analysis identifies external factors with limited efficacy in fostering sustainable business operations. These insights hold significant implications for governmental institutions in Vietnam, offering valuable guidance for updating and refining policies.

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.

The failure to achieve sustainable development in South Africa is due to the inability to deliver quality and adequate health services that would lead to the achievement of sustainable human security. As we live in an era of digital technology, Machine Learning (ML) has not yet permeated the healthcare sector in South Africa. Its effects on promoting quality health services for sustainable human security have not attracted much academic attention in South Africa and across the African continent. Hospitals still face numerous challenges that have hindered achieving adequate health services. For this reason, the healthcare sector in South Africa continues to suffer from numerous challenges, including inadequate finances, poor governance, long waiting times, shortages of medical staff, and poor medical record keeping. These challenges have affected health services provision and thus pose threats to the achievement of sustainable security. The paper found that ML technology enables adequate health services that alleviate disease burden and thus lead to sustainable human security. It speeds up medical treatment, enabling medical workers to deliver health services accurately and reducing the financial cost of medical treatments. ML assists in the prevention of pandemic outbreaks and as well as monitoring their potential epidemic outbreaks. It protects and keeps medical records and makes them readily available when patients visit any hospital. The paper used a qualitative research design that used an exploratory approach to collect and analyse data.

Low-cost housing homeownership funding for junior staffers is challenging in private sector organisations, especially in developing countries. Motivating private sector investment in junior staffers’ homeownership via a developed expanded corporate social responsibility (ECSR) may promote achieving Sustainable Development Goal 11 (SDG 11). Therefore, the study investigates the role of the ECSR framework in improving Nigeria’s private sector junior staffers’ homeownership and achieving SDG 11. Data were collected via face-to-face interviews with selected participants in six of Nigeria’s geo-political zones. The study adopted thematic analysis to analyse the collected data. Six variables emerged from the 18 re-clustered sub-variables. This includes institutionalising ECSR in low-income homeownership, housing finance for junior staffers’ homeownership, and housing incentives and stakeholders’ participation for low-income earners. The research employed six variables and 18 sub-variables to develop the improved private sector’s junior staffers’ homeownership via ECSR and achieving SDG 11 (sustainable cities and communities) and their targets. The research presents a novel approach by attempting to integrate SDG 11 with Corporate Social Housing, an extension of corporate social responsibility, especially to align the SDGs with evolving perspectives on Expanded Corporate Social Responsibility in Nigeria.