Integration of SWAT and QUAL2K for water quality modeling in a data scarce basin of Cau River basin in Vietnam

© 2019 European Regional Centre for Ecohydrology of the Polish Academy of Sciences Water quality modeling in a river basin often faces the problem of having a large number of parameters yet limited available data. The important inputs to the water quality model are pollution concentrations and discharge from river tributaries, lateral inflows and related pollution load from different sources along the river. In general, such an extensive data set is rarely available, especially for data scarce basins. This makes water quality modeling more challenging. However, integration of models may be able to fill this data gap. Selection of models should be made based on the data that is available for the river basin. For the case of Cau River basin, the SWAT and QUAL2K models were selected. The outputs of SWAT model for lateral inflows and discharges of ungauged tributaries, and the observed pollutant concentrations data and estimated pollution loads of sub-watersheds were used as inputs to the water quality model QUAL2K. The resulting QUAL2K model was calibrated and validated using recent water quality data for two periods in 2014. Four model performance ratings PBIAS, NSE, RSR and R2 were used to evaluate the model results. PBIAS index was chosen for water quality model evaluation because it more adequately accounted for the large uncertainty inherent in water quality data. In term of PBIAS, the calibration and validation results for Cau River water quality model were in the “very good” performance range with ǀPBIASǀ < 15%. The obtained results could be used to support water quality management and control in the Cau River basin

Status of water use and potential of rainwater harvesting for replacing centralized supply system in remote mountainous areas: a case study.

The failure of the centralized water supply system forced XY community to become more dependent on uncertain and unstable water sources. The results of surveying 50 households showed that 89.18% of total households depended on water collected from rivers, which contributed 58.3% of the total water volume used for the domestic demands. The average water volume consumed was 19.5 liters/person/day (l/p/d), and 86.5% of households used more than one source; 13.5% of households collected water only from rivers, and 45.94% of families had rainwater harvesting (RWH) for their activities (domestic water demand); however, RWH only provided 9.9% of total water consumption. In this study, basic methods were applied to calculate the storage tanks necessary to balance the water deficit created by drought months. Three levels of water demand (14, 20, and 30 l/p/d) can be the best choices for RWH; for a higher demand (40 and 60 l/p/d), small roof area (30-40 m2), and many people (six to seven) per family, RWH might be impractical because of unsuitable rainfall or excessively large storage tanks

Complete genome characterization of two wild-type measles viruses from Vietnamese infants during the 2014 outbreak

A large measles virus outbreak occurred across Vietnam in 2014. We identified and obtained complete measles virus genomes in stool samples collected from two diarrheal pediatric patients in Dong Thap Province. These are the first complete genome sequences of circulating measles viruses in Vietnam during the 2014 measles outbreak

Genome sequences of a novel Vietnamese bat bunyavirus

To document the viral zoonotic risks in Vietnam, fecal samples were systematically collected from a number of mammals in southern Vietnam and subjected to agnostic deep sequencing. We describe here novel Vietnamese bunyavirus sequences detected in bat feces. The complete L and S segments from 14 viruses were determined

Data Compensation with Gaussian Processes Regression: Application in Smart Building's Sensor Network

Data play an essential role in the optimal control of smart buildings' operation, especially in building energy-management for the target of nearly zero buildings. The building monitoring system is in charge of collecting and managing building data. However, device imperfections and failures of the monitoring system are likely to produce low-quality data, such as data loss and inconsistent data, which then seriously affect the control quality of the buildings. This paper proposes a new approach based on Gaussian process regression for data-quality monitoring and sensor network data compensation in smart buildings. The proposed method is proven to effectively detect and compensate for low-quality data thanks to the application of data analysis to the energy management monitoring system of a building model in Viet Nam. The research results provide a good opportunity to improve the efficiency of building energy-management systems and support the development of low-cost smart buildings

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Infertility clinics would benefit from the ability to select developmentally competent vs. incompetent oocytes using non-invasive procedures, thus improving the overall pregnancy outcome. We recently developed a classification method based on microscopic live observations of mouse oocytes during their in vitro maturation from the germinal vesicle (GV) to the metaphase II stage, followed by the analysis of the cytoplasmic movements occurring during this time-lapse period. Here, we present detailed protocols of this procedure. Oocytes are isolated from fully-grown antral follicles and cultured for 15 h inside a microscope equipped for time-lapse analysis at 37 °C and 5% CO2. Pictures are taken at 8 min intervals. The images are analyzed using the Particle Image Velocimetry (PIV) method that calculates, for each oocyte, the profile of Cytoplasmic Movement Velocities (CMVs) occurring throughout the culture period. Finally, the CMVs of each single oocyte are fed through a mathematical classification tool (Feed-forward Artificial Neural Network, FANN), which predicts the probability of a gamete to be developmentally competent or incompetent with an accuracy of 91.03%. This protocol, set up for the mouse, could now be tested on oocytes of other species, including humans

Cytoplasmic movement profiles of mouse surrounding nucleolus and not-surrounding nucleolus antral oocytes during meiotic resumption

Full-grown mouse antral oocytes are classified as surrounding nucleolus (SN) or not-surrounding nucleolus (NSN), depending on the respective presence or absence of a ring of Hoechst-positive chromatin surrounding the nucleolus. In culture, both types of oocytes resume meiosis and reach the metaphase II (MII) stage, but following insemination, NSN oocytes arrest at the two-cell stage whereas SN oocytes may develop to term. By coupling time-lapse bright-field microscopy with image analysis based on particle image velocimetry, we provide the first systematic measure of the changes to the cytoplasmic movement velocity (CMV) occurring during the germinal vesicle-to-MII (GV-to-MII) transition of these two types of oocytes. Compared to SN oocytes, NSN oocytes display a delayed GV-to-MII transition, which can be mostly explained by retarded germinal vesicle break down and first polar body extrusion. SN and NSN oocytes also exhibit significantly different CMV profiles at four main time-lapse intervals, although this difference was not predictive of SN or NSN oocyte origin because of the high variability in CMV. When CMV profile was analyzed through a trained artificial neural network, however, each single SN or NSN oocyte was blindly identified with a probability of 92.2% and 88.7%, respectively. Thus, the CMV profile recorded during meiotic resumption may be exploited as a cytological signature for the non-invasive assessment of the oocyte developmental potential, and could be informative for the analysis of the GV-to-MII transition of oocytes of other species

Using the WHO Self-Reporting Questionnaire-20 (SRQ-20) to Detect Symptoms of Common Mental Disorders among Pregnant Women in Vietnam: a Validation Study

Trang Thi Hanh Do,1 Quyen Thi Tu Bui,2 Bui Thi Thu Ha,3 Thi Minh Le,3 Vui Thi Le,3 Quynh-Chi Thai Nguyen,3 Kimberly Joyce Lakin,4 Tung Thanh Dang,5 Loi Van Bui,5 Thien Cong Le,5 An Thi Ha Tran,5 Hien Thi Thu Pham,5 Tuan Van Nguyen5 1Faculty of Environmental and Occupational Health, Hanoi University of Public Health, Hanoi, Vietnam; 2Faculty and Fundamental Sciences, Hanoi University of Public Health, Hanoi, Vietnam; 3Faculty of Social and Behavioral Sciences, Hanoi University of Public Health, Hanoi, Vietnam; 4Nossal Institute for Global Health, Melbourne School of Population and Global Health, Melbourne, Victoria, Australia; 5The National Institute of Mental Health, Bach Mai Hospital, Hanoi, VietnamCorrespondence: Quyen Thi Tu Bui, Department of Biostatistics, Faculty of Fundamental Sciences, Hanoi University of Public Health, 1A Duc Thang Street, Bac Tu Liem District, Hanoi, Vietnam, Tel +84 912 225 245, Fax +84 24 6266 2385, Email [email protected]: Detection of antenatal common mental disorders in low-resource settings like Vietnam is important and requires a reliable, valid and practical screening tool. Currently, there is no such tool validated for use among pregnant women in Vietnam. This study aims to assess the validity of the Vietnamese version of the 20-item Self Reporting Questionnaire (SRQ-20) by evaluating its reliability, factorial structure, and performance in detecting common mental disorder (CMD) symptoms, thereby identifying the optimum cut-off score for CMD screening among pregnant women in Vietnam.Participants and Methods: A total of 210 pregnant women from four rural communes participated in a face-to-face interview using the Vietnamese version of the SRQ-20, followed by a clinical diagnostic interview based on ICD-10 diagnostic criteria of CMDs. The reliability of the SRQ-20 was assessed by calculating the scale’s Cronbach’s alpha to measure internal consistency. Factor analyses were undertaken to examine the factor structure of the instrument. The Receiver Operating Characteristic (ROC) curve analysis was performed to assess the performance of the SRQ-20 against the clinical diagnosis and to identify the optimum cut-off score.Results: Internal consistency was good, with a Cronbach’s alpha of 0.87. Factor analyses resulted in a 4-factor solution. The area under the ROC curve (AUC) for detection of CMDs was 0.90. The optimum cut-off score of the SRQ-20 for detection of CMD symptoms among Vietnamese pregnant women was 5/6.Conclusion: The Vietnamese version of the SRQ-20 has the capacity to detect CMDs among pregnant women effectively and is recommended for use as a screening tool for CMDs in antenatal care settings in Vietnam.Keywords: SRQ-20, screening, common mental disorders, pregnant women, Vietna

Nitrogen removal in subsurface constructed wetland: Assessment of the influence and prediction by data mining and machine learning

Subsurface constructed wetland (SCW) appears to be an economical and environmental-friendly practice to treat nitrogen-enriched (waste) water. Nevertheless, the removal mechanisms in SCW are complicated and rather time-consuming to conduct and assessment the efficiency of new experiments. This work mined data from literature and developed the machine learning models to elucidate the effect of influent inputs and predict ammonium removal rate (ARR) in SCW treatment. 755 sets and 11 attributes were applied in four modeled algorithms, including Random forest, Cubist, Support vector machines, and K-nearest neighbors. Six out of ten input features including ammonium (NH4), total nitrogen (TN), hydraulic loading rate (HLR), the filter height (i.e., Height), aeration mode (i.e., Aeration), and types of inlet feeding (i.e., Feeding) have posed pronounced influences on the ARR. The Cubist algorithm appears the most optimal model showing the lowest RMSE i.e., 0.974 and the highest R2 i.e., 0.957. The contribution of variables followed the order of NH4, HLR, TN, Aeration, Height and Feeding corresponding to 97, 93, 71, 49, 34, and 34%, respectively. The generalization ability to forecast ARR using testing data achieved the R2 of 0.970 and the RMSE of 1.140 g/m2 d, indicating that Cubist is a reliable tool for ARR prediction. User interface and web tool of final predictive model are provided to facilitate the application for designing and developing SCW system in real practice

Effect of the optimize heart failure care program on clinical and patient outcomes – The pilot implementation in Vietnam

Background: The Ho-Chi-Minh-city Heart Institute in Vietnam took part in the Optimize Heart Failure (OHF) Care Program, designed to improve outcomes following heart failure (HF) hospitalization by increasing patient awareness and optimizing HF treatment. Methods: HF patients hospitalized with left ventricular ejection-fraction (LVEF) <50% were included. Patients received guideline-recommended HF treatment and education. Clinical signs, treatments and outcomes were assessed at admission, discharge, 2 and 6 months (M2, M6). Patients’ knowledge and practice were assessed at M6 by telephone survey. Results: 257 patients were included. Between admission and M2 and M6, heart rate decreased significantly, and clinical symptoms improved significantly. LVEF increased significantly from admission to M6. 85% to 99% of patients received education. At M6, 45% to 78% of patients acquired knowledge and adhered to practice regarding diet, exercise, weight control, and detection of worsening symptoms. High use of renin-angiotensin-aldosterone-system inhibitors (91%), mineralocorticoid-receptor-antagonists (77%) and diuretics (85%) was noted at discharge. Beta-blocker and ivabradine use was less frequent at discharge but increased significantly at M6 (from 33% to 51% and from 9% to 20%, respectively, p < 0.001). There were no in-hospital deaths. Readmission rates at 30 and 60 days after discharge were 8.3% and 12.5%, respectively. Mortality rates at 30 days, 60 days and 6 months were 1.2%, 2.5% and 6.4%, respectively. Conclusions: The OHF Care Program could be implemented in Vietnam without difficulty and was associated with high usage of guideline-recommended drug therapy. Although education was delivered, patient knowledge and practice could be further improved at M6 after discharge