Nowadays, urban ecosystems require major transformations aimed at addressing the current challenges of urbanization. In recent decades, policy makers have increasingly turned their attention to the smart city paradigm, recognizing its potential to promote positive changes. The smart city, through the conscious use of technologies and sustainability principles, allows for urban development. The scientific literature on smart cities as catalysts of public value continues to develop rapidly and there is a need to systematize its knowledge structure. Through a three-phase methodological approach, combining bibliometric, network and content analyses, this study provides a systematic review of the scientific literature in this field. The bibliometric results showed that public value is experiencing an evolutionary trend in smart cities, representing a challenging research topic for scholars. Network analysis of keyword co-occurrences identified five different clusters of related topics in the analyzed field. Content analysis revealed a strong focus on stakeholder engagement as a lever to co-create public value and a greater emphasis on social equity over technological innovation and environmental protection. Furthermore, it was observed that although environmental concerns were prioritized during the policy planning phase, their importance steadily decreased as the operational phases progressed.

This contribution questions young people’s access to digital networks at the scale of intermediate cities in Saint-Louis. Thus, it analyzes the prescriptions of digital actors responsible for the development of digital economy in relation with the orientations of the Senegal Digital 2025 strategy. This is a pretex to highlight the gaps between official political discourses and the level of deployment of digital infrastructures. The study highlights the need to repoliticize the needs of populations for broadband and very high-speed connections to promote local initiatives for youth participation in Saint-Louis. Indeed, datas relating to access and use of the Internet by young people reveal inequalities linked to household income, the disparity of infrastructure and digital equipment, and the discontinuity in neighborhood development, but also to the adaptability of the internet service marketed. Through urban and explanatory sociology mobilized through the approach of young people’s real access to the Internet, our analyzes have shown at the scale of urban neighborhoods the impact of the actions recommended by those involved in the development of populations’ access to Internet. The result is that the majority of young people are forced to access the Internet through medium-speed mobile networks.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

This paper presents an overview of the policies and efforts of the Provincial Government of Bali, Indonesia, to tackle the development of HIV/AIDS. This study considers the socio-cultural context and analyzes the factors that are most likely to influence its spread, the response of the community, and the local government’s efforts to form Provincial AIDS commission whose movement is supported by the village government and the community to suppress the spread of HIV/AIDS. The authors observe the micro factors that most determine this program, such as attitudes, behaviors, and desires of policy-making actors, stakeholders, implementing organizations, adequacy of human resources, financial funds, information, education, communication, advocacy, regional languages, the role of students, and field workers, and local culture in preventing the spread of HIV/AIDS. Therefore, this research does not focus on just one dimension in efforts to deal with this outbreak. Following the application of the public policy theory, all potential contributing elements must be addressed simultaneously. This requires a truly interdisciplinary and multisectoral approach that requires to be comprehended by policymakers in other provinces where the prevalence of HIV/AIDS is quite high. This effort also requires commitment and strong political will from levels of government.

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 study examines the effectiveness of Kazakhstan’s grant funding system in supporting research institutions and universities, focusing on the relationship between funding levels, expert evaluations, and research outputs. We analyzed 317 projects awarded grants in 2021, using parametric methods to assess publication outcomes in Scopus and Web of Science databases. Descriptive statistics for 1606 grants awarded between 2021 and 2023 provide additional insights into the broader funding landscape. The results highlight key correlations between funding, evaluation scores, and journal publication percentiles, with a notable negative correlation observed between international and national expert evaluations in specific scientific fields. A productivity analysis at the organizational level was conducted using non-parametric methods to evaluate institutional efficiency in converting funding into research output. Data were manually collected from the National Center of Science and Technology Evaluation and supplemented with publication data from Scopus and Web of Science, using unique grant numbers and principal investigators’ profiles. This comprehensive analysis contributes to the development of an analytical framework for improving research funding policies in Kazakhstan.