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Vietnam’s central coastal region is the most vulnerable and always at flood risk, severely affecting people’s livelihoods and socio-economic development. In particular, Quang Binh province is often affected by floods and storms over the year. However, it still lacks studies on flood hazard estimation and prediction tools in this area. This study aims to develop a flooding susceptibility assessment tool using various machine learning (ML) techniques namely alternating decision tree (AD Tree), logistic model tree (LM Tree), reduced-error pruning tree (REP Tree), J48 decision tree (J48) and Naïve Bayes tree (NB Tree); historical flood marks; and available data of topography, hydrology, geology, and environment considering Quang Binh province as a study area. We used flood mark locations of major flooding events in the years 2007, 2010, and 2016; and ten flood conditioning factors to construct and validate the ML models. Various validation methods, including area under the ROC curve (AUC), were used to validate and compare the models. The result of the models’ validation suggests that all models have good performance: AD Tree (AUC = 0.968), LM Tree (AUC = 0.967), REP Tree (AUC = 0.897), J48 (AUC = 0.953), and NB Tree (AUC = 0.986). Out of these, NB Tree managed to achieve the best performance in terms of flood prediction with an accuracy higher than 92 %. The final flood susceptibility map highlights 6,265 km2 (78.8 % area) with a very low flooding hazard, 391 km2 (4.9 % area) with a low flooding hazard, 224 km2 (2.8 % area) with a moderate flooding hazard, 243 km2 (3.1 %) with a high flooding hazard, and 829 km2 (10.4 % area) with very high flooding hazard. The final flooding susceptibility assessment map could add a valuable source for flood risk reduction and management activities of Quang Binh province.

Vietnam’s central coastal region is the most vulnerable and always at flood risk, severely affecting people’s livelihoods and socio-economic development. In particular, Quang Binh province is often affected by floods and storms over the year. However, it still lacks studies on flood hazard estimation and prediction tools in this area. This study aims to develop a flooding susceptibility assessment tool using various machine learning (ML) techniques namely alternating decision tree (AD Tree), logistic model tree (LM Tree), reduced-error pruning tree (REP Tree), J48 decision tree (J48) and Naïve Bayes tree (NB Tree); historical flood marks; and available data of topography, hydrology, geology, and environment considering Quang Binh province as a study area. We used flood mark locations of major flooding events in the years 2007, 2010, and 2016; and ten flood conditioning factors to construct and validate the ML models. Various validation methods, including area under the ROC curve (AUC), were used to validate and compare the models. The result of the models’ validation suggests that all models have good performance: AD Tree (AUC = 0.968), LM Tree (AUC = 0.967), REP Tree (AUC = 0.897), J48 (AUC = 0.953), and NB Tree (AUC = 0.986). Out of these, NB Tree managed to achieve the best performance in terms of flood prediction with an accuracy higher than 92 %. The final flood susceptibility map highlights 6,265 km2 (78.8 % area) with a very low flooding hazard, 391 km2 (4.9 % area) with a low flooding hazard, 224 km2 (2.8 % area) with a moderate flooding hazard, 243 km2 (3.1 %) with a high flooding hazard, and 829 km2 (10.4 % area) with very high flooding hazard. The final flooding susceptibility assessment map could add a valuable source for flood risk reduction and management activities of Quang Binh province.

Water deficiency due to climate change and the world's population growth increases the demand for the water industry to carry out vulnerability assessments. Although many studies have been done on climate change vulnerability assessment, a specific framework with sufficient indicators for water vulnerability assessment is still lacking. This highlights the urgent need to devise an effective model framework in order to provide water managers and authorities with the level of water exposure, sensitivity, adaptive capacity and water vulnerability to formulate their responses in implementing water management strategies. The present study proposes a new approach for water quantity vulnerability assessment based on remote sensing satellite data and GIS ModelBuilder. The developed approach has three layers: (1) data acquisition mainly from remote sensing datasets and statistical sources; (2) calculation layer based on the integration of GIS-based model and the Intergovernmental Panel on Climate Change's vulnerability assessment framework; and (3) output layer including the indices of exposure, sensitivity, adaptive capacity and water vulnerability and spatial distribution of remote sensing indicators and these indices in provincial and regional scale. In total 27 indicators were incorporated for the case study in Vietnam based on their availability and reliability. Results show that the most water vulnerable is the South Central Coast of the country, followed by the Northwest area. The novel approach is based on reliable and updated spatial-temporal datasets (soil water stress, aridity index, water use efficiency, rain use efficiency and leaf area index), and the incorporation of the GIS-based model. This framework can then be applied effectively for water vulnerability assessment of other regions and countries.

Water deficiency due to climate change and the world's population growth increases the demand for the water industry to carry out vulnerability assessments. Although many studies have been done on climate change vulnerability assessment, a specific framework with sufficient indicators for water vulnerability assessment is still lacking. This highlights the urgent need to devise an effective model framework in order to provide water managers and authorities with the level of water exposure, sensitivity, adaptive capacity and water vulnerability to formulate their responses in implementing water management strategies. The present study proposes a new approach for water quantity vulnerability assessment based on remote sensing satellite data and GIS ModelBuilder. The developed approach has three layers: (1) data acquisition mainly from remote sensing datasets and statistical sources; (2) calculation layer based on the integration of GIS-based model and the Intergovernmental Panel on Climate Change's vulnerability assessment framework; and (3) output layer including the indices of exposure, sensitivity, adaptive capacity and water vulnerability and spatial distribution of remote sensing indicators and these indices in provincial and regional scale. In total 27 indicators were incorporated for the case study in Vietnam based on their availability and reliability. Results show that the most water vulnerable is the South Central Coast of the country, followed by the Northwest area. The novel approach is based on reliable and updated spatial-temporal datasets (soil water stress, aridity index, water use efficiency, rain use efficiency and leaf area index), and the incorporation of the GIS-based model. This framework can then be applied effectively for water vulnerability assessment of other regions and countries.

Flood vulnerability is increasing in Vietnam due to rapid population growth, urbanization, and industrial development. Although there has been a significant increase in studies on flood risk assessments at the local scales in Vietnam, there remains a lack of tools developed for use at the national scale. In response to this need, we sourced and analyzed flood damage data from 1989 to 2015 in Vietnam's national disaster database in order to assess the flood risk of Vietnam's regions and provinces. The study proposes a new approach, multiple linear regression-TOPSIS, to analyze the disaster data. Our findings show that the North Central Coast is the most vulnerable region, followed by the Mekong Delta. Nghe An province has the highest flood risk score while Ho Chi Minh city has the lowest flood risk. This assessment tool provides accessible risk information for decision-makers and planners to classify the most at-risk areas and has practical implications for flood risk management at the national and local scales. The study also provides a new method for analyzing Vietnam's national disaster database, which may suggest potential applications for disaster loss databases in other countries and regions.

Mesenchymal stem cells are widely used in many pre-clinical and clinical settings. Despite advances in molecular technology; the migration and homing activities of these cells in in vivo systems are not well understood. Labelling mesenchymal stem cells with gold nanoparticles has no cytotoxic effect and may offer suitable indications for stem cell tracking. Here, we report a simple protocol to label mesenchymal stem cells using 80 nm gold nanoparticles. Once the cells and particles were incubated together for 24 h, the labelled products were injected into the rat subretinal layer. Micro-computed tomography was then conducted on the 15th and 30th day post-injection to track the movement of these cells, as visualized by an area of hyperdensity from the coronal section images of the rat head. In addition, we confirmed the cellular uptake of the gold nanoparticles by the mesenchymal stem cells using transmission electron microscopy. As opposed to other methods, the current protocol provides a simple, less labour-intensive and more efficient labelling mechanism for real-time cell tracking. Finally, we discuss the potential manipulations of gold nanoparticles in stem cells for cell replacement and cancer therapy in ocular disorders or diseases.

Immersion and interactivity are a major focus when creat- ing gaming applications, as technology has improved and enabled the creation of larger and more detailed virtual environments the need for more engaging NPCs (non-playable characters) is also required. Many games utilise a form of dialogue tree when conversing with characters within a gaming application, allowing the user to choose their question- s/responses. While this method does provide a dynamic conversation system, it is quite a one-sided level of interactivity with the NPC simply responding to the current question without it a ecting the conversation on a whole. We present a novel dialogue system that explores the emo- tional state of the NPC to provide a more complex form of dialogue tree, termed EDTree (Emotional Dialogue Tree). Based on user actions, the interactions between the user and the NPC are enriched by the emo- tional state of the NPC. Utilising this system will provide an immersive experience based around improved believability of virtual characters. To demonstrate the e ectiveness of our approach, we show an example of a training system that explores the use of gaming technology and the proposed EDTree.