Apply deep learning to improve the question analysis model in the Vietnamese question answering system

Question answering (QA) system nowadays is quite popular for automated answering purposes, the meaning analysis of the question plays an important role, directly affecting the accuracy of the system. In this article, we propose an improvement for question-answering models by adding more specific question analysis steps, including contextual characteristic analysis, pos-tag analysis, and question-type analysis built on deep learning network architecture. Weights of extracted words through question analysis steps are combined with the best matching 25 (BM25) algorithm to find the best relevant paragraph of text and incorporated into the QA model to find the best and least noisy answer. The dataset for the question analysis step consists of 19,339 labeled questions covering a variety of topics. Results of the question analysis model are combined to train the question-answering model on the data set related to the learning regulations of Industrial University of Ho Chi Minh City. It includes 17,405 pairs of questions and answers for the training set and 1,600 pairs for the test set, where the robustly optimized BERT pre-training approach (RoBERTa) model has an F1-score accuracy of 74%. The model has improved significantly. For long and complex questions, the mode has extracted weights and correctly provided answers based on the question’s contents

Remote Sensing for Monitoring Surface Water Quality in the Vietnamese Mekong Delta: The Application for Estimating Chemical Oxygen Demand in River Reaches in Binh Dai, Ben Tre

In this study, the method of Fault Movement Potential (FMP) proposed by Lee et al. (1997) is used to assess the Surface water resources played a fundamental role in sustainable development of agriculture and aquaculture. They were the main sectors contributing to economic development in the Vietnamese Mekong Delta. Monitoring surface water quality was also one of the essential missions especially in the context of increasing freshwater demands and loads of wastewater fluxes. Recently, remote sensing technology has been widely applied in monitoring and mapping water quality at a regional scale replacing traditional field-based approaches. The aims of this study were to assess the application of the Landsat 8 (OLI) images for estimating Chemical Oxygen Demand (COD) as well as detecting spatial changes of the COD concentration in river reaches of the Binh Dai district, Ben Tre province, a downstream area of the delta. The results indicated the significant correlation (R=0.89) between the spectral reflectance values of Landsat 8 and the COD concentration by applying the Artificial Neuron Network (ANN) approach. In addition, the spatial distribution of the COD concentration was found slightly exceeded the national standard for irrigation according to the B1 column of QCVN 08:2015.References Ackerman S., Richard F., Kathleen S., Yinghui L., Chris M., Liam G., Bryan B., and Paul M., 2010. Discriminating clear-sky from cloud with MODIS algorithm theoretical basis document (MOD35). Ali Sheikh A.A., Ghorbanali A., and Nouri N., 2007. Coastline change detection using remote sensing. International Journal of Environmental Science and Technology 4(1), 61-66. Bonansea M., María C. R., Lucio P., and Susana F., 2015. Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina). Remote Sensing of Environment 158, 28-41. 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Effect of nanosilica/chitosan hybrid on leaf blast and blight diseases of rice in Vietnam

Nanosilica/chitosan (NSi/CTS) hybrid material was prepared using nanosilica (32.5 nm) from rice husk ash (RHA) and chitosan (CTS), and characterized by transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD). The obtained NSi/CTS was used for protection of rice leaf from blast disease (Piriculariaoryzae) and blight disease (Xanthomonasoryzae). Results indicated that foliar spraying of NSi/CTS with 100 ppm NSiand150 ppm CTS were effective against blast and blight diseases on rice (Oryza spp.). The leaf blast disease index (DI) (1.49 %) and the blight DI (1.45 %) were significantly decreased compared with control of 8.08 % and 9.29 %, respectively at 14th day after the first treatment. Thus, NSi/CTS hybrid material is promising to use for controlling plant diseases, particularly for rice

Risk Factors for Severe Pneumonia According to WHO 2005 Criteria Definition Among Children <5 Years of Age in Thai Binh, Vietnam: A Case–Control Study

International audienc

Strongyloides stercoralis seroprevalence in Vietnam

Strongyloidiasis is a neglected tropical disease caused by the roundworm Strongyloides stercoralis affecting 30-100 million people worldwide. Many Southeast-Asian countries report a high prevalence of S. stercoralis infection, but there are little data from Vietnam. Here, we evaluated the seroprevalence of S. stercoralis related to geography, sex and age in Vietnam through serological testing of anonymized sera. Sera (n = 1710, 1340 adults and 270 children) from an anonymized age-stratified serum bank from four regions in Vietnam between 2012 and 2013 were tested using a commercial Strongyloides ratti immunoglobulin G ELISA. Seroreactivity was found in 29·1% (390/1340) of adults and 5·5% (15/270) of children. Male adults were more frequently seroreactive than females (33·3% vs. 24·9%, P = 0·001). The rural central highlands had the highest seroprevalence (42·4% of adults). Seroreactivity in the other regions was 29·9% (Hue) and 26·0% and 18·2% in the large urban centres of Hanoi and Ho Chi Minh City, respectively. We conclude that seroprevalence of S. stercoralis was high in the Vietnamese adult population, especially in rural areas

Hydropower dams, river drought and health effects: a detection and attribution study in the lower Mekong Delta Region

The upstream construction of hydropower dams may drastically intensify climate change impacts due to changing the natural river flood-drought cycle and reducing the amount of water that flows into the lower Mekong Delta river, leading to hydrological and environmental health impacts. However, until now the influence of drought on residents’ health in the lower MDR, where river drought is highly sensitive to recently built hydropower plants, has not been examined. The objectives of this study are, for the first time, to detect the health impacts of river drought on residents and to evaluate the contribution of hydropower dams to the impacts of drought on health in the lower Mekong Delta Region (MDR). We applied the multi-step approaches of a Detection and Attribution study. First, we detected the effects of the river drought on the risk of hospitalization using a Multivariable Fractional Polynomials algorithm (MFP). Second, we linked the long-term changes of the river water level (RWL) to the operation of the first hydropower dam in the upper MDR using the interrupted time-series model (ITS). Finally, we quantified the hospitalizations and related economic loss attributed to the river drought. The results show that the percentage changes in risk of all-cause, respiratory, and renal hospitalizations attributed to the river drought were 2%, 2%, and 7%. There were significant reductions in average level and trend of the RWL during the post-1995 period, when the first hydropower dam began operation in the upper MDR, even though the cumulative rainfall in the MDR had not changed. The all-cause hospitalizations attributed to the river drought were 1134 cases during the period 1995–2014, which resulted in total additional cost at two provincial hospitals of US $360,385. This current study demonstrates the link between hydropower dams, river drought, and health impacts. As the MDR is highly vulnerable to climate change, these findings about the devastating impacts of hydropower dams and environmental change have important implications for the lives of downstream residents