The following paper assesses the relationship between electricity consumption, economic growth, environmental pollution, and Information and Communications Technology (ICT) development in Kazakhstan. Using the structural equation method, the study analyzes panel data gathered across various regions of Kazakhstan between 2014 and 2022. The data were sourced from official records of the Bureau of National Statistics of Kazakhstan and include all regions of Kazakhstan. The chosen timeframe includes the period from 2014, which marked a significant drop in oil prices that impacted the overall economic situation in the country, to 2022. The main hypotheses of the study relate to the impact of electricity consumption on economic growth, ICT, and environmental sustainability, as well as ICT’s role in economic development and environmental impact. The results show electricity consumption’s positive effect on economic growth and ICT development while also revealing an increase in pollutant emissions (emissions of liquid and gaseous pollutants) with economic growth and electricity consumption. The development of ICT in Kazakhstan has been revealed to not have a direct effect on reducing pollutant emissions into the environment, raising important questions about how technology can be leveraged to mitigate environmental impact, whether current technological advancements are sufficient to address environmental challenges, and what specific measures are needed to enhance the environmental benefits of ICT. There is a clear necessity to integrate sustainable practices and technologies to achieve balanced development. These results offer important insights into the relationships among electricity consumption, technology, economic development, and environmental issues. They underscore the complexity and multidimensionality of these interactions and suggest directions for future research, especially in the context of finding sustainable solutions for balanced development.

The paper proposes a methodology for the analysis and evaluation of the traffic scheme of Bulgarian cities. The authors combine spatial, network, and socio-economic analyses of cities with transport operators’ financial-economic evaluation, sociological studies of transport habits, and the possibilities of new information technologies for transport modeling (such as geographic information systems). The model proposes several approaches to optimize the municipality’s transport scheme. It results from a new need to improve urban traffic, the quality of transport services, and the integration of urban transport into the regional economy of Stara Zagora municipality. It presents a description, analysis, and outline of the opportunities for developing urban transport connectivity and mobility in Stara Zagora municipality. The research results show a deficit of transport connectivity between the different parts of the city, reflecting on the regional economy’s development and the efficiency of the environment and the population.

The Government of Indonesia has modernized the toll road transaction system by implementing the multi-lane free-flow (MLFF) project, set to operate commercially by the end of 2024. This project leverages Global Navigation Satellite System (GNSS) technology to identify vehicles using toll roads and establish a transaction mechanism that allows the MLFF Project Company to charge road users according to distance, vehicle category, and tariff levels. The project has result in a complex business arrangement between the Indonesia National Toll Road Authority (INTRA), Toll Road Companies (TRCs), and the MLFF Project Company. The aim of this paper is to review the regulatory and institutional framework of the MLFF project and analyze its challenges. The methodology employed is a qualitative framework for legal research, utilizing international literature reviews and current regulatory frameworks. The study assesses the proposed transaction architecture of the project and identifies commercial, political, and other risks associated with its implementation. Based on the analysis, the research identifies opportunities for regulatory improvements and better contracting arrangements. This research provides valuable insights into the regulatory landscape and offers policy recommendations for the Government to mitigate the identified risks. This contribution is significant to the academic field as it enhances understanding regulatory and institutional challenges in implementing advanced toll road systems.

Malaria is an infectious disease that poses a significant global health threat, particularly to children and pregnant women. Specifically, in 2020, Rampah Village, Kutambaru sub-district, Langkat Regency, North Sumatra Province, Indonesia, reported 22 malaria cases, accounting for 84% of the local cases. This study aims to develop a malaria prevention model by leveraging community capital in Rampah Village. A mixed-method sequential explanatory approach, combining quantitative and qualitative methods, was employed. Quantitative data were collected through questionnaires from a sample of 200 respondents and analyzed using structural equation modeling (SEM) with Smart PLS (Partial Least Squares) software. The qualitative component utilized a phenomenological design, gathering data through interviews. Quantitative findings indicate that natural capital significantly influences malaria prevention principles. There is also a positive and significant relationship between developmental capital and malaria prevention. Cultural capital shows a positive correlation with malaria prevention, as does social capital. The qualitative phase identified cultural capital within the Karo tribe, such as ‘Rakut si Telu,’ which signifies familial bonds fostering mutual aid and respect. The results of this study are crucial for formulating policies and redesigning community-capital-based malaria prevention programs. These programs can be effectively implemented through cross-sectoral collaboration among health departments, local government, and community members. Malaria is a communicable disease threatening global health, particularly affecting children and pregnant women. In 2020, there were 229 million cases of Malaria worldwide, resulting in 409,000 deaths. In Indonesia, specifically in North Sumatra’s Langkat Regency, Kutambaru District, Rampah Village had 22 cases (84%). The purpose of this research is to formulate a Malaria prevention model using community resources in Rampah Village, Kutambaru District, Langkat Regency. The study employed a mixed-methods sequential explanatory approach, combining quantitative and qualitative methods. Quantitative data was collected through questionnaires, with 200 respondents, and structural equation modeling (SEM) analysis using smart PLS (Partial Least Squares) software. Qualitative data was gathered through interviews. The research findings showed a positive relationship between cultural modalities and Malaria prevention (p = 0.000) with a path coefficient T-value of 12.500. The cultural modality and Malaria prevention relationship were significantly positive (p = 0.000) with a path coefficient T-value of 3.603. A positive and significant correlation also exists between development modalities and Malaria prevention (p = 0.011) with a path coefficient T-value of 2.555. Qualitative research revealed the Rakut si Telu cultural modality of the Karo tribe, meaning that family-based social connections create a sense of helping and respecting one another. The Orat si Waluh cultural modality represents daily life practices in the Karo tribe as a form of community-based Malaria prevention.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.