| 1. |
- Tran, Ngoc Hieu, et al.
(författare)
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Genetic profiling of Vietnamese population from large-scale genomic analysis of non-invasive prenatal testing data
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The under-representation of several ethnic groups in existing genetic databases and studies have undermined our understanding of the genetic variations and associated traits or diseases in many populations. Cost and technology limitations remain the challenges in performing large-scale genome sequencing projects in many developing countries, including Vietnam. As one of the most rapidly adopted genetic tests, non-invasive prenatal testing (NIPT) data offers an alternative untapped resource for genetic studies. Here we performed a large-scale genomic analysis of 2683 pregnant Vietnamese women using their NIPT data and identified a comprehensive set of 8,054,515 single-nucleotide polymorphisms, among which 8.2% were new to the Vietnamese population. Our study also revealed 24,487 disease-associated genetic variants and their allele frequency distribution, especially 5 pathogenic variants for prevalent genetic disorders in Vietnam. We also observed major discrepancies in the allele frequency distribution of disease-associated genetic variants between the Vietnamese and other populations, thus highlighting a need for genome-wide association studies dedicated to the Vietnamese population. The resulted database of Vietnamese genetic variants, their allele frequency distribution, and their associated diseases presents a valuable resource for future genetic studies.
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| 2. |
- Ha, Duong Hai, et al.
(författare)
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Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping
- 2021
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Ingår i: Water resources management. - : Springer. - 0920-4741 .- 1573-1650. ; 35:13, s. 4415-4433
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Tidskriftsartikel (refereegranskat)abstract
- In this study, the AdaBoost, MultiBoost and RealAdaBoost methods were combined with the Quadratic Discriminant Analysis method to develop three new GIS-based Machine Learning ensemble models, i.e., ABQDA, MBQDA, and RABQDA for groundwater potential mapping in the Dak Nong Province, Vietnam. In total, 227 groundwater wells and 12 conditioning factors (infiltration, rainfall, river density, topographic wetness index, sediment transport index, stream power index, elevation, aspect, curvature, slope, soil, and land use) were used for this study. Performance of the models was evaluated using the Area Under the Receiver Operating Characteristics Curve AUC (AUC) and several other performance metrics. The results showed that the ABQDA model that achieved AUC = 0.741 was superior to the other models in producing an accurate map of groundwater potential for the Dak Nong Province. The models and potential maps produced here can help policymakers and water resources managers to preserve an optimal exploit from these vital resources.
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| 3. |
- Pham, Binh Thai, et al.
(författare)
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A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:6
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Tidskriftsartikel (refereegranskat)abstract
- Determination of shear strength of soil is very important in civilengineering for foundation design, earth and rock fill dam design, highway and airfield design,stability of slopes and cuts, and in the design of coastal structures. In this study, a novel hybrid softcomputing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) wasdeveloped and used to estimate the undrained shear strength of soil based on the clay content (%),moisture content (%), specific gravity (%), void ratio (%), liquid limit (%), and plastic limit (%). Inthis study, the experimental results of 127 soil samples from national highway project Hai Phong-Thai Binh of Vietnam were used to generate datasets for training and validating models. Pearsoncorrelation coefficient (R) method was used to evaluate and compare performance of the proposedmodel with single RF model. The results show that the proposed hybrid model (RF-PSO) achieveda high accuracy performance (R = 0.89) in the prediction of shear strength of soil. Validation of themodels also indicated that RF-PSO model (R = 0.89 and Root Mean Square Error (RMSE) = 0.453) issuperior to the single RF model without optimization (R = 0.87 and RMSE = 0.48). Thus, theproposed hybrid model (RF-PSO) can be used for accurate estimation of shear strength which canbe used for the suitable designing of civil engineering structures.
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| 4. |
- Hoa, Nguyen Thi Quynh, et al.
(författare)
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Numerical Study of a Wide-Angle and Polarization-Insensitive Ultrabroadband Metamaterial Absorber in Visible and Near-Infrared Region
- 2019
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Ingår i: IEEE Photonics Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1943-0655. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- An ultrabroadband metamaterial absorber structure based on a periodic array of metallic-dielectric multilayered conical frustums is numerically investigated and proposed. The metamaterial absorber indicated an absorptivity of higher than 90%, which covered the visible and near-infrared region at 480-1480 nm, and a relative absorption bandwidth of 102%. The high absorptivity can be maintained with large incident angles up to 60 degrees under both transverse electric and transverse magnetic polarizations. Furthermore, the proposed absorber exhibits polarization insensitivity owing to its rotational symmetry structure. Compared with the previously reported ultrabroadband metamaterial absorbers, the design in this work indicates high practical feasibility in terms of a compact structure for a large bandwidth, a wide incident angle, and polarization insensitivity, thereby suggesting its promising application, for example, in solar cells and thermal emitters.
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| 5. |
- Le, Vui Thi, et al.
(författare)
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Sex Ratio at Birth in Vietnam : Results From Data in CHILILAB HDSS, 2004 to 2013
- 2017
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Ingår i: Asia-Pacific journal of public health. - : SAGE PUBLICATIONS INC. - 1010-5395 .- 1941-2479. ; 29:5 Suppl., s. 25S-34S
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Tidskriftsartikel (refereegranskat)abstract
- This study aimed to explore the association of demographic and socioeconomic characteristics and imbalanced sex ratio at birth (SRB) in Chi Linh district, Hai Duong. The data were collected from a longitudinal study using a community-based periodic, referred as Chi Linh Health and Demographic Surveillance System (CHILILAB HDSS) during 2004 to 2013. A total of 7568 children were analyzed. Results showed that SRB in Chi Linh dramatically increased to the imbalanced sex ratio (114.6 boys to 100 girls) by 2013. SRB was associated with birth order and sex of preceding siblings. SRB was extremely high among families without any sons (136/100). SRB was highest among families having third or more children (175/100). Imbalanced SRB was more likely to occur among women working in small business/homemakers and others, women who attained high education level, and women in wealthy households. We suggested further efforts to tackle imbalanced SRB in periurban areas in Vietnam.
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| 6. |
- Luu, Chinh, et al.
(författare)
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Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:7, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- Vietnam has been extensively affected by floods, suffering heavy losses in human life andproperty. While the Vietnamese government has focused on structural measures of flood defence such as levees and early warning systems, the country still lacks flood risk assessment methodologies and frameworks at local and national levels. In response to this gap, this study developed a flood risk assessment framework that uses historical flood mark data and a high- resolution digital elevation model to create an inundation map, then combined this map with exposure and vulnerability data to develop a holistic flood risk assessment map. The case study is the October 2010 flood event in Quang Binh province, which caused 74 deaths, 210 injuries, 188,628 flooded properties, 9019 ha of submerged and damaged agricultural land, and widespread damages to canals, levees, and roads. The final flood risk map showed a total inundation area of 64348 ha, in which 8.3% area of low risk, 16.3% area of medium risk, 12.0% area of high risk, 37.1% area of very high risk, and 26.2% area of extremely high risk. The holistic flood risk assessment map of QuangBinh province is a valuable tool and source for flood preparedness activities at the local scale.
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| 7. |
- Nguyen, Phong Tung, et al.
(författare)
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Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique : The DakNong Province Case-study, Vietnam
- 2020
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Ingår i: International Journal of Environmental Research and Public Health. - Switzerland : MDPI. - 1661-7827 .- 1660-4601. ; 17:7
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Tidskriftsartikel (refereegranskat)abstract
- The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.
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| 8. |
- Nguyen, Phong Tung, et al.
(författare)
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Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:7
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Tidskriftsartikel (refereegranskat)abstract
- Groundwater is one of the most important sources of fresh water all over the world, especially in those countries where rainfall is erratic, such as Vietnam. Nowadays, machine learning (ML) models are being used for the assessment of groundwater potential of the region. Credal decision trees (CDT) is one of the ML models which has been used in such studies. In the present study, the performance of the CDT has been improved using various ensemble frameworks such as Bagging, Dagging, Decorate, Multiboost, and Random SubSpace. Based on these methods, five hybrid models, namely BCDT, Dagging-CDT, Decorate-CDT, MBCDT, and RSSCDT, were developed and applied for groundwater potential mapping of DakLak province of Vietnam. Data of 227 groundwater wells of the study area were utilized for the construction and validation of the models. Twelve groundwater potential conditioning factors, namely rainfall, slope, elevation, river density, Sediment Transport Index (STI), curvature, flow direction, aspect, soil, land use, Topographic Wetness Index (TWI), and geology, were considered for the model studies. Various statistical measures, including area under receiver operating characteristic (AUC) curve, were applied to validate and compare the performance of the models. The results show that performance of the hybrid CDT ensemble models MBCDT (AUC = 0.770), BCDT (AUC = 0.731), Dagging-CDT (AUC = 0.763), Decorate-CDT (AUC = 0.750), and RSSCDT (AUC = 0.766) improved significantly in comparison to the single CDT (AUC = 0.722) model. Therefore, these developed hybrid models can be applied for better ground water potential mapping and groundwater resources management of the study area as well as other regions of the world.
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| 9. |
- Nguyen, Phong Tung, et al.
(författare)
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Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping
- 2020
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Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:7
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Tidskriftsartikel (refereegranskat)abstract
- Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans.
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| 10. |
- Nguyen, Viet-Tien, et al.
(författare)
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GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility : A Case Study at Da Lat City, Vietnam
- 2019
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 11:24
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Tidskriftsartikel (refereegranskat)abstract
- Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.
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