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Surface sediments from estuarine and coast of CanGio wetland (CGW) of Hochiminh City, Vietnam, were investigated to identify the spatial and temporal variations of polycyclic aromatic hydrocarbons (PAHs). The total PAHs showed wide variation but similar to patterns observed (F = 0.901, p = 0.46) in LongTau (31 ± 77 ng/g dry weight, n = 13), SoaiRap (53 ± 81, n = 4), ThiVai (60 ± 62, n = 10) estuaries, and coastal areas (112 ± 211, n = 9). A decreasing trend in the wet season (F = 8.8, p = 0.01) reflected that inland sources such as wastewater discharged and atmospherically transported contaminants contributed to PAHs in sediments. The risk posed by the PAHs in the coastal and estuarine areas of CGW is still negligible. The present study provides baseline data, which can be used for regular monitoring and future strategy of environmental protection for the study area.

International audience; Irrigation systems contribute worldwide to the provision of a wide range of services on which the survival and well-being of humanity depend. They are of primary importance in Vietnam where about 90% of the water used is for irrigation and aquaculture and where agriculture is the largest employer and a major contributor to the national GDP and to the income of the low-salary households. Nevertheless, irrigation systems have recently been subjected to several issues including increasing demand, pollution, under-investment, depletion of resources or environmental changes. Any mitigation measure against these issues needs to be sustainable with respect to the very diverse uses of the water, the changing conditions upstream and downstream, and the somewhat conflicting objectives carried out by land-use/agricultural planning on one hand and urbanization and society well-being on the other. This need of sustainability requires the design of innovative tools to tackle these issues. This work aims at exploring the usage of Agent-Based Modelling coupled with a tangible and interactive interface in order to enhance interactions between stakeholders and support the evaluation of various alternatives of the management of the Bac Hung Hai irrigation system.

The challenges of rapid urbanisation are multi-faceted. During the past few decades, many different concepts about the urban sustainability under the threats of climate change (CC) have been studied. Resilience is among the most recent concepts, used for the first time in the 2000s and has ever since attracted greater attention and interests from both academia and urban authorities, and has subsequently applied in urban planning. In order to develop and manage cities to be resilient, many studies show that the Remote Sensing (RS) and Geographical Information System (GIS) could be useful to architects, urban planners and municipalities in the urban planning process. This study selected the case of capital city Phan Rang-Thap Cham, the most urbanized area of Ninh Thuan province, in the center of Vietnam; it has been known as one of the coastal cities that are affected by CC, drought, storms, floods and sea level rise. Among those phenomena, drought—the most serious hazard was picked up for exploration recently. We utilized RS and GIS to examine city’s Land Use and Land Cover (LULC), then extract the most relevant drought-induced factors in the years of 1991, 1997 and 2019, such as: Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Normalised Difference Built-up Index (NDBI). The study results show inter-relationships and transformations of the three indicators in Ninh Thuan context, where urban land temperature and Urban Heat Island (UHI) are raised by built-up areas and in contrast, reduced by vegetated areas. As a result, urban green spaces would be a driver to mitigate urban heat and moving toward drought resilience.

Surface sediments from estuarine and coast of CanGio wetland (CGW) of Hochiminh City, Vietnam, were investigated to identify the spatial and temporal variations of polycyclic aromatic hydrocarbons (PAHs). The total PAHs showed wide variation but similar to patterns observed (F = 0.901, p = 0.46) in LongTau (31 ± 77 ng/g dry weight, n = 13), SoaiRap (53 ± 81, n = 4), ThiVai (60 ± 62, n = 10) estuaries, and coastal areas (112 ± 211, n = 9). A decreasing trend in the wet season (F = 8.8, p = 0.01) reflected that inland sources such as wastewater discharged and atmospherically transported contaminants contributed to PAHs in sediments. The risk posed by the PAHs in the coastal and estuarine areas of CGW is still negligible. The present study provides baseline data, which can be used for regular monitoring and future strategy of environmental protection for the study area.

Classifiers in stock market are an interesting and challenging research topic in machine learning. A large research has been conducted for classifying in stock market by using different approaches in machine learning. This research paper presents a detail study on integrating sentiment classifier and technical indicator classifier. The research subject is investigated to classify a stock into one of three labels being top, neutral or bottom. First, using technical indicators such as relative strength index (RSI), money flow index (MFI) and relative volatility index (RVI) to classify stock, then using bagging of learning machine to classify the stock. Second, using sentiment data to classify the stock. Third, integrating technical indicator and sentiment classifiers to build hybrid classifier. In this study, hybrid machine learning by combining sentiment and technical indicator classifiers is proposed. We applied this proposal hybrid classifier for five stocks in VN30. The empirical results show hybrid classifier stock has more power than single technical indicator classifier or sentiment classifier.