The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

This paper provides a disaster resilience-based approach. For the definition of the approach, a three-step method (definition of components, analysis of the resilience pillars and definitions of resilience-based actions) has been followed. To validate the approach, an application scenario for mitigating the COVID-19 pandemic is provided in the paper. The proposed approach contributes to stimulating the co-responsibility quadruple helix of actors in the implementation of actions for disaster management. Moreover, the approach is adaptable and flexible, as it can be used to manage different kinds of disasters, adjusting or changing itself to meet specific needs.

The construction industry is responsible for over 40% of global energy consumption and one-third of global greenhouse gas emissions. Generally, 10%–20% of energy is consumed in the manufacturing and transportation stages of materials, construction, maintenance, and demolition. The way the construction industry to deal with these impacts is to intensify sustainable development through green building. The author uses the latest Green Building Certification Standard in Indonesia as the Green Building Guidelines under the Ministry of Public Works and People’s Housing (PUPR) Regulation No. 01/SE/M/2022, as a basis for evaluating existing office buildings or what is often referred to as green retrofit. Structural Equation Modeling-Partial Least Squares (SEM-PLS) is used by the authors to detail the factors influencing the application of green building by analyzing several variables related to the problem studied, which are used to build and test statistical models of causal models. From this study, it is concluded that the most influential factors in the implementation of green retrofitting on office buildings are energy savings, water efficiency, renewable energy use, the presence of green building socialization programs, cost planning, design planning, project feasibility studies, material cost, use of the latest technology applications, and price fluctuations. With the results of this research, there is expected to be shared awareness and concern about implementing green buildings and green offices as an initiative to present a more energy-efficient office environment, save operating costs, and provide comfort to customers.

Africa has an extensive and varied cultural history that includes works of art, music, literature, customs, and historical locations. These cultural resources are essential for creating identities, promoting social cohesiveness, and advancing economic development. However, for these institutions to have the greatest impact on the world and contribute to sustainable development, they must be managed and engaged effectively. Exploring the management of cultural institutions in Africa and their potential for global impact and sustainable development is the goal of this research study. The study relies on the extensive review of available literature, case studies, and in-depth interviews with key informants, and data obtained, subjected to content and thematic analyses. It aims to uncover flexible management techniques that can improve the global reach and sustainable development of African cultural institutions by examining successful models and cutting-edge approaches. The results of this study will help those responsible for administering Africa’s cultural institutions to formulate practical guidelines and policy recommendations. Africa can further establish its cultural identity, advance cultural diplomacy, and utilize its cultural capital to propel social and economic advancement by utilizing the potential of these institutions for global impact and sustainable development.

Air pollution in Jakarta has become a severe concern in the last four months. IQAir, in August 2023, revealed that the level of air pollution had reached 161 points on the Air Pollution Standard Index (APSI). The negative impact on society has placed air pollution as a concern for environmental safety and survival in danger. This condition will encourage the development of a national policy agenda to integrate environmental welfare through various energy efficiency channels. This research analyzes the relationship between air pollutant elements that can reduce air quality. The analysis includes pollutant intensity measured by APSI per unit of pollutant as a measure of efficiency. The aim is to observe energy use, which causes an increase in pollutant levels. This research utilizes dynamic system modeling to produce relationships between parameters to produce factors that cause pollution. The parameters used are motorized vehicles, waste burning in landfills, industry, and power plants. The results of historical behavioral tests and statistical suitability tests show that the behavior is suitable for the short and long term. The simulation results show that the pollution level will worsen by the end of 2027, a hazardous condition for society. The optimistic scenario simulation model proposes immediate counter-measures to reduce pollution to 45.01, the ideal condition. To accelerate improvements in air quality, the Government can plan policies to reduce the use of coal by power plants and industry, as well as the use of electric motorized vehicles, resulting in an ideal reduction in pollution by 2024. In conclusion, pollution can be reduced effectively if the Government firmly implements policies to maintain that air quality remains stable below 50 points.