The Song Hong-Yinggehai (SH-Y) and Qiongdongnan (Qi) basins together form one of the largest Cenozoic sedimentary basins in SE Asia. Here we present new records based on the analysis of seismic data, which we compare to geochemical data derived from cores from Ocean Drilling Program (ODP) Site 1148 in order to derive proxies for continental weathering and thus constrain summer monsoon intensity.The SH-Y Basin started opening during the Late Paleocene–Eocene. Two inversion phases are recognized to have occurred at c. 34 Ma and c. 15 Ma. The Qi Basin developed on the northern, rifted margin of South China Sea, within which a large canyon developed in a NE–SW direction.Geochemical and mineralogical data show that chemical weathering has gradually decreased in SE Asia after c. 25 Ma, whereas physical erosion became stronger, especially after c. 12 Ma. Summer monsoon intensification drove periods of faster erosion after 3–4 Ma and from 10–15 Ma, although the initial pulse of eroded sediment at 29.5–21 Ma was probably triggered by tectonic uplift because this precedes monsoon intensification at c. 22 Ma. Clay mineralogy indicates more physical erosion together with high sedimentation rates after c. 12 Ma suggesting a period of strong summer monsoon in the Mid-Miocene.
The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these areas have been converted from the natural wetland to the human-made wetland. It is anticipated that the conversion rate continues to increase due to economic development and urbanization. Therefore, monitoring and assessment of the wetland are essential for the coastal vulnerability assessment and geo-ecosystem management. The aim of this study is to propose and verify a new deep learning approach to interpret 9 of 19 coastal wetland types classified in the RAMSAR and MONRE systems for the Tien Yen estuary of Vietnam. Herein, a Resnet framework was integrated into the U-Net to optimize the performance of the proposed deep learning model. The Sentinel-2, ALOS-DEM, and NOAA-DEM satellite images were used as the input data, whereas the output is the predefined nine wetland types. As a result, two ResU-Net models using Adam and RMSprop optimizer functions show the accuracy higher than 85%, especially in forested intertidal wetlands, aquaculture ponds, and farm ponds. The better performance of these models was proved, compared to Random Forest and Support Vector Machine methods. After optimizing the ResU-Net models, they were also used to map the coastal wetland areas correctly in the northeastern part of Vietnam. The final model can potentially update new wetland types in the southern parts and islands in Vietnam towards wetland change monitoring in real time.
Motivation
Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine learning methods have emerged as prominent tools to support analyzing and predicting ADRs. Nonetheless, there are still remaining challenges in ADR studies.
Results
In this paper, we summarized ADR data sources and review ADR studies in three tasks: drug-ADR benchmark data creation, drug–ADR prediction and ADR mechanism analysis. We focused on machine learning methods used in each task and then compare performances of the methods on the drug–ADR prediction task. Finally, we discussed open problems for further ADR studies.
Availability
Data and code are available at https://github.com/anhnda/ADRPModels.
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