A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education

Machine reading comprehension has been an interesting and challenging task in recent years, with the purpose of extracting useful information from texts. To attain the computer ability to understand the reading text and answer relevant information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for the task of multiple-choice reading comprehension in Vietnamese Textbooks which contain the reading articles for students from Grade 1 to Grade 12. This dataset has 699 reading passages which are prose and poems, and 5,273 questions. The questions in the new dataset are not fixed with four options as in the previous version. Moreover, the difficulty of questions is increased, which challenges the models to find the correct choice. The computer must understand the whole context of the reading passage, the question, and the content of each choice to extract the right answers. Hence, we propose the multi-stage approach that combines the multi-step attention network (MAN) with the natural language inference (NLI) task to enhance the performance of the reading comprehension model. Then, we compare the proposed methodology with the baseline BERTology models on the new dataset and the ViMMRC 1.0. Our multi-stage models achieved 58.81% by Accuracy on the test set, which is 5.34% better than the highest BERTology models. From the results of the error analysis, we found the challenge of the reading comprehension models is understanding the implicit context in texts and linking them together in order to find the correct answers. Finally, we hope our new dataset will motivate further research in enhancing the language understanding ability of computers in the Vietnamese language

Neuromuscular Blockade Agents Reversal with Sugammadex Compared to Neostigmine in the Living Kidney Donors

Backround: The reversation of NMBA (neuromuscular blocking agents) prevents numerous postoperative complications, increases quality of recovery and decreases the time, expenditure spending in hospital. The choice of medicine used to reverse NMBA depends  considered as a key fators to gain the best outcome and to avoid the side effects. Aim: To evaluate the postoperative effect on muscle relaxation reversal and side effects of sugammadex 2 mg/kg versus the combination of  neostigmine and atropine sulfate in the living kidney donors. MethodS: A randomised controlled trial on 70 patients undergoing living kidney donation surgery were allocated to 2 groups. Patients in group I (SUGA) were reversed with sugammadex 2 mg/kg and in group II (NEO/ATR) with the combination of neostigmine and atropine sulfat. Results: With 35 patients in each group, the study results showed that after 3 mintutes of reversal patients reaching TOF value ≥ 0.9 in group SUGA is 91.4%, after 5 minutes 100% of patients in group SUGA reached TOF value ≥ 0.9 . In group NEO/ATR after 3 minutes 28.6% patients reached TOF ≥ 0.9 and 40% patients reached TOF≥ 0.9 after 5 minutes. The difference in percentage of patients reaching TOF ≥ 0.9 after 3 minutes, 5 minutes of reversal between two groups is significant (p<0.05). After 10 minutes, 100% patients in both group got TOF ≥ 0.9. Time to exutubation of group SUGA was 249.43 ± 81.75 seconds and it was 456.29 ± 146.45 seconds in group NEO/ATR. Nausea, bradycardia, and increased phlegm production in group NEO/ATR was 22.9%; 28.5%; 25.7% respectively; while those side effects were not met in group SUGA, the difference was significant (p<0.05). Conclusion: The muscle relaxation reversal effect of sugammadex was faster than that of neostigmine, the duration TOF ≥ 0.9 and the time to extubation was significantly faster. Sugammadex did not cause hemodynamic changes before and after muscle relaxation reversal, neostigmine resulted in the bradycardia, increased phlegm secreting and other side effects. The renal function after 24 hours postoperatively of two groups was similar

HMU fluorinze mouthwash enhances enamel remineralization: An in vitro study

BACKGROUND: Fluoride therapy has long been used extensively to prevent dental caries. Fluoride appears in variety of dental care products such as mouthrinse, dentifrice, gel, etc. HMU fluorinze is the first mouthwash containing fluoride in Vietnam. AIM: This research was conducted to evaluate the efficacy of HMU Fluorinze mouthwash on remineralizing enamel in laboratory conditions. METHODS: 20 third molars teeth were cleaned and covered with nail polish , except for a 3x3 mm square on their buccal surfaces. These teeth went through two steps: demineralization using Coke and remineralization for 20 days: 1) using standard calcifying solution (control group) and 2) using standard calcifying solution + HMU Fluorinze mouthwash 2 times/day (experimental group). The mineralization index of enamel structure after demineralization and remineralization was assessed by DIAGNOdent pen 2190. RESULTS: The mineralization indexes of the control group and experimental group at baseline were 3.65 ± 0.76 and 3.35 ± 0.64, after demineralization were in turn of 21.78 ± 4.48 and 20.25 ± 2.26; and after remineralization were 6.30 ± 1.03 and 3.90 ± 1.24. The different figures  between the two groups after remineralization shows statistical significance (p<0.01). Group B using HMU fluorinze mouthwash after 20 days did not differ from the original results (p = 0.272), in contrast with the control group (p<0.01). CONCLUSIONS: HMU fluorinze mouthwash has better mineralization effect than standard calcifying solution

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

An upgrading feature-based opinion mining model on Vietnamese product reviews

Feature-based opinion mining and summarizing (FOMS) of reviews is an interesting issue in the opinion mining field. SentiWordNet is an useful lexical resource for opinion mining, especially for FOMS. In this paper, an upgrading FOMS model on Vietnamese reviews on mobile phone products is described. Feature words and opinion words were extracted based on some Vietnamese syntactic rules. Extracted feature words were grouped by using HAC clustering and semi-supervised SVM-kNN classification. Customers’ opinion orientation and summarization on features was determined by using a VietSentiWordNet, which had been extended from an initial VietSentiWordNet. Experiments on feature extraction and opinion summarization on features are showed

A feature-based opinion mining model on product reviews in Vietnamese

Feature-based opinion mining and summarizing (FOMS) of reviews is an interesting issue in opinion mining field. In this paper, we propose an opinion mining model on Vietnamese reviews on mobile phone products. Explicit/Implicit feature-words and opinion-words were extracted by using Vietnamese syntax rules as same as synonym feature words were grouped into a feature, which belongs to the feature dictionary. Customers’ opinion orientations and summarization on features were determined by using VietSentiWordNet and suitable formulas

A Hybrid Approach to Word Order Transfer in the English-to-Vietnamese Machine Translation

Word Order transfer is a compulsory stage and has a great effect on the translation result of a transfer-based machine translation system. To solve this problem, we can use fixed rules (rulebased) or stochastic methods (corpus-based) which extract word order transfer rules between two languages. However, each approach has its own advantages and disadvantages. In this paper, we present a hybrid approach based on fixed rules and Transformation-Based Learning (or TBL) method. Our purpose is to transfer automatically the English word orders into the Vietnamese ones. The learning process will be trained on the annotated bilingual corpus (named EVC: English-Vietnamese Corpus) that has been automatically word-aligned, phrase-aligned and POStagged. This transfer result is being used for the transfer module in the English-Vietnamese transfer-based machine translation system.

Evaluation of genetic diversity and structure of Vietnamese goat populations using multi locus microsatellite markers

Goats are an important component of the Vietnamese livestock industry but little is known about their genetic diversity, differentiation and population structure. We evaluated genetic diversity of seven indigenous goat populations located in different provinces (CoVBV (Co goats from Ba Vi), (CoVSL) Co goats from Son La, CoVTH (Co goats from Thanh Hoa), CoVNT (Co goats from Ninh Thuan), CoVHg (Co goats from Ha Giang), BTVBV (Bach Thoa goat from Ba Vi) and BTVNT (Bach Thoa goats from Ninh Thuan)) and used microsatellite markers to assess the gene flow and population structure among them. The basic diversity measures viz. allelic diversity and heterozygosity were low in Vietnam goats as compared to West Asian and European goats. The overall mean FIS was significantly positive in Co goats (0.203) whereas it was only 0.003 in Bach Thao goats. Considerable genetic differentiation existed among goat populations from different provinces with the mean global FST, FIT and FIS estimated to be 0.083, 0.178 and 0.104 respectively. The phylogenetic tree constructed on pair-wise allele sharing distances revealed clustering of the two Bach Thao goat populations together. Among the Co goat populations, Son La (CoVSL) and Ha Giang (CovHG) clustered together distinctly while Thanh Hoa (CoVTH), Ba Vi (CoVBV) and Ninh Thuan (CoVNT) populations clustered intermediately. The results of multi-dimensional scaling plot of pairwise FST showed a similar picture except that Co-Ninh Thuan (CoVNT) was differentiated from other Co goat populations. When genotype assignment was performed at breed level, the percent correct assignment of Bach Thao goats (93.7%–96.2%) was consistently higher than Co goats (86.8%–88.1%) across the three evaluated methods. When genotype assignment at sub-population level was done, both Co (CoVNT) and Bach Thao (BTHNT) goat populations in Ninh Thuan province were assigned correctly while the percent correct assignment differed among goat populations from other provinces. AMOVA and Bayesian clustering analysis revealed the existence of significant sub-population structure and varying levels of admixture within Co goats among different provinces. The present study is the first report on genetic diversity and structure of indigenous goat populations raised in different provinces of Vietnam

Mapping multi-decadal mangrove extent in the northern coast of Vietnam using Landsat time-series data on Google Earth Engine platform

A pixel-based algorithm for multi-temporal Landsat (TM/ETM+/OLI/OLI-2) imagery between 1990 and 2022 monitored mangrove dynamics and detected their changes in the three provinces (i.e., Thai Binh, Nam Dinh and Hai Phong), which are located on the Northern coast of Vietnam, through the Google Earth Engine (GEE) cloud computing platform. Results showed that the mangrove area in the study area decreased from 2960 ha in 1990 to 2408 ha in 1995 and then significantly increased to 4435 ha in 2000 but later declined to 3502 ha in 2005. The mangrove areas experienced an increase from 4706 ha in 2010 to 10,125 ha in 2020 and reached a highest peak of 10,630 ha in 2022. In 2022, Hai Phong province had the largest area of mangrove (3934 ha), followed by Nam Dinh (3501 ha) and Thai Binh (3195 ha) provinces. The overall accuracies for 2020 and 2022 were 94.94% and 91.98%, while the Kappa coefficients were 0.90 and 0.84, respectively. The mangrove restoration programs and policies by the Vietnamese government and local governments are the key drivers of this increase in mangroves in the three provinces from 1990 to 2022. The results also demonstrated that the combination of Landsat time series images, a pixel-based algorithm, and the GEE platform has a high potential for monitoring long-term change of mangrove forests during 32 years in the tropics. Moreover, the obtained mangrove forest maps at a 30-m spatial resolution can serve as a useful and up-to-date dataset for sustainable management and conservation of these mangrove forests in the Red River Delta, Vietnam