This research presents a comprehensive model for enhancing the road network in Thailand to achieve high efficiency in transportation. The objective is to develop a systematic approach for categorizing roads that aligns with usage demands and responsible agencies. This alignment facilitates the creation of interconnected routes, which ensure clear responsibility demarcation and foster efficient budget allocation for road maintenance. The findings suggest that a well-structured road network, combined with advanced information and communication technology, can significantly enhance the economic competitiveness of Thailand. This model not only proposes a framework for effective road classification but also outlines strategic initiatives for leveraging technology to achieve transportation efficiency and safety.

In the contemporary landscape characterized by technological advancements and a progressive economic environment, the utilization of currency has undergone a paradigm shift. Despite the growing prevalence of digital currency, its adoption among the Vietnamese population faces several challenges, including limited financial literacy, concerns over security, and resistance to change from traditional cash-based transactions. This research aims to identify these challenges and propose solutions to encourage the widespread use of digital currency in Vietnam. This research adopts a quantitative approach, utilizing Likert scale questionnaires, with a dataset of 330 records. The interrelationships among variables are analyzed using partial least squares structural equation modeling (PLS-SEM). The analysis results substantiate the viability of the research model, confirming the hypotheses. The findings demonstrate a positive relationship and the significance impact of factors such as perceived usefulness (PU), perceived ease of use (PEOU), perceived trust (PT), social influence (SI), openness to innovation (OI), and financial knowledge (FK) to intention to use digital currency (IUDC). Thereby aiming to inform policymakers, industry stakeholders, and the wider community, fostering a deeper understanding of consumer behavior and providing solutions to enhance the adoption of digital currency in the evolving landscape of digital finance.

With the rapid increase in electric bicycle (e-bikes) use, the rate of associated traffic accidents has also escalated. Prior studies have extensively examined e-bike riders’ injury risks, yet there is a limited understanding of how their behavior contributes to these accidents. This study aims to explore the relationship between e-bike riders’ risk-taking behaviors and the incidence of traffic accidents, and to propose targeted safety measures based on these insights. Utilizing a mixed-methods approach, this research integrates quantitative data from traffic accident reports and qualitative observations from naturalistic studies. The study employs a binary logistic regression model to analyze risk factors and uses observational data to substantiate the model findings. The analysis reveals that assertive driving behaviors among e-bike riders, such as running red lights and speeding, significantly contribute to the high rate of accidents. Moreover, the lack of protective gear and inadequate safety training are identified as critical factors increasing the risk of severe injuries. The study concludes that comprehensive policy interventions, including stricter enforcement of traffic laws and mandatory safety training for e-bike riders, are essential to mitigate the risks associated with e-bike use. The findings advocate for an integrated approach to urban traffic management that enhances the safety of all road users, particularly vulnerable e-bike riders.

Brazil occupies a prominent position as one of the largest domestic air passenger markets globally. In May 2019, OAG Aviation Worldwide Limited (OAG), a renowned global travel data provider, ranked Brazil as the world’s 6th largest domestic market. This study identifies and meticulously analyses statistical trends in how service levels affect passenger demand on domestic air routes in Brazil. To that end, it employs a panel-data gravity model incorporating service as an instrumental variable. The findings confirm the influence of traditional gravity explanatory variables, while also contributing novel insights into the impact of service levels on domestic routes. The analysis reveals that, while factors such as income and distance play a fundamental role in shaping domestic demand, level of service emerges as a crucial determinant on regional connections. Overall, the statistics suggest growing divergences between Brazilian airlines and regional air transport. Accordingly, substantial changes are necessary in both government policies and the services offered by the airline industry in order to harness the full potential of Brazil’s domestic air transport passenger market and foster regional development.

The research aimed to: 1) analyze components and indicators of digital transformation leadership among school administrators, 2) assess their leadership needs, and 3) develop mechanism models to promote this leadership. A mixed-method approach was applied, involving three sample groups: 8 experts, 406 administrators, and 7 experts. Data collection tools included semi-structured interviews, leadership scales, needs assessments, and focus group discussions, with analysis performed through construct validity testing, needs assessment, and content analysis. The findings revealed: 1) The components and indicators of digital transformation leadership showed structural validity, as confirmed by the model's alignment with empirical data (Chi-Square = 82.3, df = 65, p = 0.072, CFI = 0.998, TLI = 0.997, RMR = 0.00965, RMSEA = 0.0256). 2) Among the leadership components, "innovative knowledge" ranked highest in need (PNImodified = 0.075), followed by "ideological influence" (0.066), "consideration of individuality" (0.055), "intellectual stimulation" (0.052), and "inspiration" (0.053). 3) Mechanism models for promoting leadership emphasized enhancing these five components to strengthen administrators' skills in applying technology, managing teaching and development plans, and fostering innovation. Administrators were encouraged to tailor strategies to individual needs, inspire personnel, and create a commitment to organizational change and development. These mechanisms aim to equip administrators to effectively lead transformations, motivate staff, and drive educational institutions to adapt and thrive in evolving environments.

The area of lake surface water is shrinking rapidly in Central Asia. We explore anthropogenic and climate factors driving this trend in Shalkar Lake, located in the Aral Sea region in Kazakhstan, Central Asia. We employ the Landsat satellite archive to map interannual changes in surface water between 1986 and 2021. The high temporal resolution of our dataset allows us to analyze the water surface data to investigate the time series of surface water change, economic and agricultural activities, and climate drivers like precipitation, evaporation, and air temperature. Toward this end, we utilize dynamic linear models (DLM). Our findings suggest that the shrinking of Shalkar Lake does not exhibit a systemic trend that could be associated with climate factors. Our empirical analysis, adopted to address local conditions, reveals that water reduction in the area is related to human interventions, particularly agricultural activities during the research period. On the other hand, the retrospectively fitted values indicate a semi-regular periodicity despite anthropogenic factors. Our results demonstrate that climate factors still play an essential role and should not be disregarded. Additionally, considering long-term climate projections in environmental impact assessment is crucial. The projected increase in temperatures and the corresponding decline in lake size highlights the need for proactive measures in managing water resources under changing climatic conditions.