Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.

This paper explores how blockchain technology, widely known as the backbone of cryptocurrencies, can be harnessed to address limitations of traditional water quality monitoring (WQM) systems. Blockchain offers a decentralized, tamper-proof ledger that enables secure, transparent, and traceable data management across distributed networks. When applied to water quality monitoring, blockchain facilitates real-time data acquisition, enhances data integrity, and enables smart contracts for automated regulatory compliance and alerts. These features not only improve the accuracy and efficiency of WQM systems but also build public trust in the reported data. Key insights from current research and pilot applications highlight blockchain’s capacity to integrate with IoT devices for real-time sensing, support adaptive water governance, and empower local stakeholders through decentralized control and transparent access to information. The implications for policy and practice are significant: blockchain-based WQM can support stronger regulatory enforcement, encourage cross-sector collaboration, and provide a robust digital foundation for sustainable water management in smart cities and rural areas alike. As such, this review paper positions blockchain as a transformative tool in the digital transition toward more resilient and equitable water management systems.

Purpose: In recent years, agricultural activities in the Mekong Delta have frequently faced the potential risks of drought, saline intrusion and unusually heavy rainfall because of climate change, leading to a decline in crop yield. Therefore, this study aimed to establish rice planting seasons in An Giang, an upper-located province in the Mekong Delta.Design/methodology/approach: The impacts of seasonal variation on the key rice seasons were simulated using the Food and Agriculture Organization-crop model for the OM6976 rice variety grown in the study area. For the simulation, the model combined crop, soil, weather and crop management data.Findings: The results show that seasonal variation because of changes in weather factors leads to alternation in crop yields across the study area. Specifically, the spring and summer rice planting seasons are advanced by one to two weeks compared with the baseline, and crop yield increased by 5.9% and 4.2%, respectively. Additionally, planting for the autumn–winter rice season on 3 August increased crop yield by up to 8.1%.Originality/value: In general, rice planting seasons that account for weather factor changes effectively reduce production costs and optimise production.

Livelihoods and sustainable livelihoods play a very essential role in coastal fishing communities in Vietnam. In this paper, based on the sustainable livelihood model, we evaluate the current situation of coastal fishing livelihoods in the North central region of Vietnam with case study for Nghe An province and identifies favorable and unfavorable factors for accessing livelihood development resources by fishermen. For the research methods, secondary data from selected governmental agencies were gathered, and 200 fishermen were interviewed to collect primary data. The results of this research indicate that the income of fishermen is quite good but not stable due to their dependence on marine resources. Moreover, the types of livelihoods in the coastal zone of Nghe An province face certain difficulties with their specific occupational characteristics such as lack of capital, no stable job, reduced marine resources, weather dependence, lack of labor. Based on these, 3 groups of solutions and policies are proposed to promote sustainable livelihood development for coastal fishermen in Nghe An province, such as renewing fishing activities, promoting specialization and associating in maritime manufacturing and processing, as well as developing a variety of occupational groups associated with the exploitation of marine resources.

This paper developes the teacher education programs (TEPs) based on the PDCA (Plan-Do-Check-Act) cycle. The development of TEPs is a complex process that requires a model with a clear philosophy, specific methods and approaches. In the context of Vietnam's comprehensive educational reform aimed at meeting national and international standards, the study designs a model for developing TEPs oriented toward competency development and outcome standards.Using methods of analysis, evaluation, stakeholder consultation, and benchmarking, this study clarifies the principles of model design, its components, advantages, limitations, and proposes solutions for further development. The findings indicate that the model addresses many gaps in TEPs development faced by Vietnamese universities. However, adjustments and improvements are needed in the future to achieve higher effectiveness. This study makes a significant contribution to the body of knowledge on curriculum development in higher education based on the PDCA cycle in the context of TEPs in Vietnam.