The sustainable development of Madeira Island necessitates the implementation of more precise and targeted planning strategies to address its regional challenges. Given the urgency of this issue within the context of sustainability, planning approaches must be grounded in and reinforced by a comprehensive array of thematic studies to fully grasp the complexities involved. This research leverages Geographic Information Systems (GIS) to analyze land use and occupancy patterns and their evolution within the municipality of Machico on Madeira Island. The study provides a nuanced perspective on the urban structure’s stagnation in the region, while concurrently highlighting the dynamic shifts in agricultural practices. Furthermore, it elucidates the transformation of predominant native vegetation within the municipality from 1990 to 2018. Notably, the research underscores the alarming decline in native vegetation due to anthropogenic activities, emphasizing the need for more rigorous monitoring by regional authorities to safeguard and preserve these valuable landscapes, habitats, and ecosystems.

In the dynamic landscape of modern education, it is essential to understand and recognize the psychological habits that underpin students’ learning processes. These habits play a crucial role in shaping students’ learning outcomes, motivation, and overall educational experiences. This paper shifts the focus towards a more nuanced exploration of these psychological habits in learning, particularly among secondary school students. We propose an innovative assessment model that integrates multimodal data analysis with the quality function deployment theory and the subjective-objective assignment method. This model employs the G-1-entropy value method for an objective evaluation of students’ psychological learning habits. The G-1-entropy method stands out for its comprehensive, objective, and practical approach, offering valuable insights into students’ learning behaviors. By applying this method to assess the psychological aspects of learning, this study contributes to educational research and informs educational reforms. It provides a robust framework for understanding students’ learning habits, thereby aiding in the development of targeted educational strategies. The findings of this study offer strategic directions for educational management, teacher training, and curriculum development. This research not only advances theoretical knowledge in the field of educational psychology but also has practical implications for enhancing the quality of education. It serves as a scientific foundation for educators, administrators, and policymakers in shaping effective educational practices.

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).

This study seeks to explore the information value of free cash flow (FCF) on corporate sustainability and investigate the moderating effects of board gender diversity and firm size on the association between FCF and corporate sustainability of Thai listed companies. The dataset consists of companies listed on the Stock Exchange of Thailand (SET) in 2022. Multivariate regression analysis is executed in this study. Subsequently, PROCESS macro served to evaluate the proposed hypotheses. This study found that FCF has a significant positive relationship with corporate sustainability. As well, board gender diversity and firm size both moderate the relationship between FCF and corporate sustainability, such that the positive effect of FCF on corporate sustainability is stronger when the proportion of female boards diminishes, while firm size is smaller. However, when firms have a larger proportion of females on the boards of directors for all levels of firm size, free cash flow indicates that there is no statistically significant effect on corporate sustainability. This study contributes to FCF and sustainability literature by understanding the extent of corporate sustainability.

This study examines the relationship between macroeconomic determinants and education levels in eight selected African oil-exporting countries (AOECs) over the period 2000–2022. Drawing on human capital theory, the paper scrutinizes the impact of factors such as income inequality, health outcome, economic growth, human development, unemployment, education expenditure, institutional quality, and energy consumption on education levels. Employing robust estimation techniques such as fixed effects (FE), random effects (RE), pooled mean group (PMG) and cross-section autoregressive distributed lag model (CS-ARDL), the study unveils vital static and dynamic interactions among these determinants and education levels. Findings reveal notable positive and significant connections between education levels and some of the variables—human capital development, institutional quality, government expenditure on education, and energy consumption, while income inequality demonstrates a consistent negative relationship. Unexpectedly, health outcomes exhibit a negative impact on education levels, warranting further investigation. Furthermore, the analysis deepens understanding of long-run and short-run relationships, highlighting, for example, the contradictory impact of gross domestic product (GDP) and unemployment on education levels in AOECs. Finally, the study recommends targeted human development programs, enhanced public investment in education, institutional reforms for good governance, and sustainable energy infrastructure development.