This study investigated the influence of infrastructure spending, government debt, and inflation on GDP in South Africa from 1995 to 2023. Motivated by the need for sustainable growth amid fiscal and inflationary pressures, this research addresses gaps in understanding how these factors shape economic performance. The primary objective was to assess these variables’ individual and combined effects on GDP and offer policy recommendations. Using an ARDL model, the study explored long- and short-term relationships among the variables. Results indicate that infrastructure spending positively impacts GDP, promoting long-term growth, while government debt hinders GDP in both short and long runs. Moderate inflation supports growth, but excessive inflation poses risks. These findings imply the need for targeted infrastructure investments, strict debt management practices, and inflation control measures to sustain economic stability and growth. Policy recommendations include expanding public investment in productive infrastructure, implementing fiscal rules to prevent unsustainable debt levels, and maintaining inflation within a controlled range. Ultimately, these policies could help South Africa build a resilient, balanced economy that addresses both immediate growth needs and long-term stability.

This scientific study aims to thoroughly assess the current status and evaluate key indicators influencing healthcare and the workforce in selected European Union (EU) member states. Building upon this ambitious research agenda, we focused on a comprehensive descriptive analysis of selected indicators within the healthcare sector, including healthcare financing schemes, overall employment in healthcare and social care, the number of graduates in healthcare (including physicians and general practitioners), as well as migration patterns within the healthcare sector. The data forming the basis of this analysis were systematically gathered from Organization for Economic Co-operation and Development (OECD) and Eurostat databases. Subsequently, we conducted a robust correlation analysis to explore the intricate relationships among these indicators. Our research endeavour aimed to identify and quantify the impact of these indicators on each other, with a focus on their implications for overall healthcare and the workforce in the respective countries. Based on the findings obtained, we derived several significant conclusions and recommendations. For instance, we identified that increasing employment in the healthcare sector may be associated with the overall quality of healthcare provision in a given country. These findings have important implications for policymaking and decision-making at the EU level. Therefore, we recommend that policymakers in these countries consider implementing measures to further develop the healthcare sector while also helping to retain and attract qualified professionals in the healthcare industry. Such recommendations could include improving healthcare infrastructure, incentivizing professional education and further training in the healthcare sector, and implementing policies to support healthcare provision more broadly.

Leadership behavior is a critical component of effective management, significantly influencing organizational success. While extensive research has examined key success factors in road management, the specific role of leadership behaviors in road usage charging (RUC) management remains underexplored. This study addresses this gap by identifying and analyzing leadership behavior dimensions and their impact on management performance within the RUC context. Using a mixed-methods approach, focus group discussions with industry practitioners were conducted to define eight leadership behavior dimensions: Central-Level Leadership Guidance (LE1), Local-Level Leadership Guidance (LE2), Central-Level Leadership Commitment (LE3), Local-Level Leadership Commitment (LE4), Subordinate Understanding from Central-Level Leadership (LE5), Subordinate Understanding from Local-Level Leadership (LE6), Work Motivation (LE7), and Understanding Rights and Obligations (LE8). These dimensions were further validated through a quantitative survey distributed to 138 professionals involved in RUC management in Vietnam, with the data analyzed using structural equation modeling (SEM) and partial least squares (PLS) estimation. The findings revealed that LE3 (Central-Level Leadership Commitment) had the strongest direct impact on management performance (MP) and mediated the relationships between other leadership dimensions and management outcomes. This study contributes to the theoretical understanding of leadership in RUC management by highlighting the centrality of leadership commitment and offering practical insights for improving leadership practices to enhance organizational performance in infrastructure management.

To achieve the electrification of private vehicles, it is urgent to develop public charging infrastructure. However, choosing the most beneficial type of public charging infrastructure for the development of a country or region remains challenging. The municipal decision’s implementation requires considering various perspectives. An important aspect of energy development involves effectively integrating and evaluating public charging infrastructure. While car charging facilities have been thoroughly studied, motorcycle charging facilities have been neglected despite motorcycles being a vital mode of transportation in many countries. The study created a hybrid decision-making model to evaluate electric motorcycle charging infrastructure. Firstly, a framework for evaluating electric motorcycle charging infrastructure was effectively constructed through a literature survey and expert experience. Secondly, decision-makers’ opinions were gathered and integrated using Bayesian BWM to reach a group consensus. Thirdly, the performance of the alternative solutions was evaluated by exploring the gaps between them and the aspiration level through modified VIKOR. An empirical analysis was conducted using examples of regions/countries with very high rates of motorcycle ownership worldwide. Finally, comparative and sensitivity analyses were conducted to demonstrate the practicality of the proposed model. The study’s findings will aid in addressing municipal issues and achieving low-carbon development objectives in the area.

Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).