| 1. |
- Bentham, James, et al.
(författare)
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A century of trends in adult human height
- 2016
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Ingår i: eLIFE. - 2050-084X. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- Being taller is associated with enhanced longevity, and higher education and earnings. We reanalysed 1472 population-based studies, with measurement of height on more than 18.6 million participants to estimate mean height for people born between 1896 and 1996 in 200 countries. The largest gain in adult height over the past century has occurred in South Korean women and Iranian men, who became 20.2 cm (95% credible interval 17.522.7) and 16.5 cm (13.319.7) taller, respectively. In contrast, there was little change in adult height in some sub-Saharan African countries and in South Asia over the century of analysis. The tallest people over these 100 years are men born in the Netherlands in the last quarter of 20th century, whose average heights surpassed 182.5 cm, and the shortest were women born in Guatemala in 1896 (140.3 cm; 135.8144.8). The height differential between the tallest and shortest populations was 19-20 cm a century ago, and has remained the same for women and increased for men a century later despite substantial changes in the ranking of countries.
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| 2. |
- Bentham, James, et al.
(författare)
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A century of trends in adult human height
- 2016
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Ingår i: eLIFE. - : eLife Sciences Publications Ltd. - 2050-084X. ; 5
-
Tidskriftsartikel (refereegranskat)abstract
- Being taller is associated with enhanced longevity, and higher education and earnings. We reanalysed 1472 population-based studies, with measurement of height on more than 18.6 million participants to estimate mean height for people born between 1896 and 1996 in 200 countries. The largest gain in adult height over the past century has occurred in South Korean women and Iranian men, who became 20.2 cm (95% credible interval 17.5–22.7) and 16.5 cm (13.3– 19.7) taller, respectively. In contrast, there was little change in adult height in some sub-Saharan African countries and in South Asia over the century of analysis. The tallest people over these 100 years are men born in the Netherlands in the last quarter of 20th century, whose average heights surpassed 182.5 cm, and the shortest were women born in Guatemala in 1896 (140.3 cm; 135.8– 144.8). The height differential between the tallest and shortest populations was 19-20 cm a century ago, and has remained the same for women and increased for men a century later despite substantial changes in the ranking of countries.
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| 3. |
- Chu, Dinh-Toi, et al.
(författare)
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An update on obesity : Mental consequences and psychological interventions
- 2019
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Ingår i: Diabetes & Metabolic syndrome. - : Elsevier. - 1871-4021 .- 1878-0334. ; 13:1, s. 155-160
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Forskningsöversikt (refereegranskat)abstract
- Besides physical consequences, obesity has negative psychological effects, thereby lowering human life quality. Major psychological consequences of this disorder includes depression, impaired body image, low self-esteem, eating disorders, stress and poor quality of life, which are correlated with age and gender. Physical interventions, mainly diet control and energy balance, have been widely applied to treat obesity; and some psychological interventions including behavioral therapy, cognitive behavioral therapy and hypnotherapy have showed some effects on obesity treatment. Other psychological therapies, such as relaxation and psychodynamic therapies, are paid less attention. This review aims to update scientific evidence regarding the mental consequences and psychological interventions for obesity. (c) 2018 Diabetes India. Published by Elsevier Ltd. All rights reserved.
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| 4. |
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| 5. |
- Dung, Nguyen Van, et al.
(författare)
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Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin
- 2021
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Ingår i: Geomatics, Natural Hazards and Risk. - : Taylor & Francis. - 1947-5705 .- 1947-5713. ; 12:1, s. 1688-1714
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Tidskriftsartikel (refereegranskat)abstract
- In this study, two novel hybrid models namely Bagging-based Rough Set (BRS) and AdaBoost-based Rough Set (ABRS) were used to generate landslide susceptibility maps of Son La hydropower reservoir basin, Vietnam. In total, 186 past landslide events and twelve landslides affecting factors (slope degree, slope aspect, elevation, curvature, focal flow, river density, rainfall, aquifer, weathering crust, lithology, fault density and road density) were considered in the modeling study. The landslide data was split into training (70%) and testing (30%) for the model's development and validation. One R feature selection method was used to select and prioritize the landslide affecting factors based on their importance in model prediction. Performance of the hybrid developed models was evaluated and also compared with single rough set (RS) and support vector machine (SVM) models using various standard statistical measures including area under the curve (AUC)-receiver operating characteristics (ROC) curve. The results show that the developed hybrid model BRS (AUC = 0.845) is the most accurate model in comparison to other models (ABRS, SVM and RS) in predicting landslide susceptibility. Therefore, the BRS model can be used as an effective tool in the development of an accurate landslide susceptibility map of the hilly area.
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| 6. |
- Nguyet, To Thi, et al.
(författare)
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Enhanced response characteristics of NO2 gas sensor based on ultrathin SnS2 nanoplates : Experimental and DFT study
- 2024
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Ingår i: Sensors and Actuators A-Physical. - : Elsevier. - 0924-4247 .- 1873-3069. ; 373
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Tidskriftsartikel (refereegranskat)abstract
- Layered-metal dichalcogenides with extraordinary characteristics of vast surface area, tunable bandgap and superior adsorption capability enable the potential for application in gas sensors. However, the synthesis of effective material for enhanced response performance remains a challenge. Herein, we exploited a fascinating sensitivity and selectivity towards NO2 gas detection using SnS2 nanoflakes prepared via the hydrothermal method. SnS2 nanoflakes with a thickness of 25 nm and an average diameter of approximately 500 nm show the potential for the detection of NO2 gas at low concentrations of ppb levels. The sensing properties of the SnS2 sensors were investigated for different concentrations of NO2 at various operating temperatures. The sensor exhibits the highest gas-sensing response of 161 at 250 οC upon exposure to 5 ppm of NO2 gas with fast response and recovery times. In addition, the sensor shows excellent selectivity with a low detection limit of ppb level. The electronic structure and gas-sensing mechanism are elucidated via finding density of states, charge density, and band structure based on DFT study which is calculated by the Vienna ab-initio simulation package (VASP). The considerable small adsorption energy reveals a physisorption of the NO2 molecules on the SnS2 surface (-0.174 eV), indicating the SnS2 nanoflakes are intriguing candidates for the speedy detection of NO2 gas.
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| 7. |
- Thanh Hoan, Nguyen, et al.
(författare)
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Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam
- 2022
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Ingår i: CMES - Computer Modeling in Engineering & Sciences. - : Tech Science Press. - 1526-1492 .- 1526-1506. ; 131:3, s. 1431-1449
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Tidskriftsartikel (refereegranskat)abstract
- Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best tool for accurate prediction. In this study, we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging (SOM) and Bagging (M5P) to predict historical water levels in the study area. Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees (REPT), which is a benchmark ML model. The data of 19 years period was divided into 70:30 ratio for the modeling. The data of the period 1/2000 to 5/2013 (which is about 70% of total data) was used for the training and for the period 5/2013 to 12/2018 (which is about 30% of total data) was used for testing (validating) the models. Performance of the models was evaluated using standard statistical measures: Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that the performance of all the developed models is good (R2 > 0.9) for the prediction of water levels in the study area. However, the Bagging-based hybrid models are slightly better than another model such as REPT. Thus, these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.
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| 8. |
- 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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| 9. |
- 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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| 10. |
- Ngo, Trinh Quoc, et al.
(författare)
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Landslide Susceptibility Mapping Using Single Machine Learning Models : A Case Study from Pithoragarh District, India
- 2021
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Ingår i: Advances in Civil Engineering / Hindawi. - : Hindawi Publishing Corporation. - 1687-8086 .- 1687-8094. ; 2021
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Tidskriftsartikel (refereegranskat)abstract
- Landslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning (ML) models have been used in landslide studies for better landslide susceptibility mapping and risk management. In the present study, we have used three single ML models, namely, linear discriminant analysis (LDA), logistic regression (LR), and radial basis function network (RBFN), for landslide susceptibility mapping at Pithoragarh district, as these models are easy to apply and so far they have not been used for landslide study in this area. The main objective of this study is to evaluate the performance of these single models for correctly identifying landslide susceptible zones for their further application in other areas. For this, ten important landslide affecting factors, namely, slope, aspect, curvature, elevation, land cover, lithology, geomorphology, distance to rivers, distance to roads, and overburden depth based on the local geoenvironmental conditions, were considered for the modeling. Landslide inventory of past 398 landslide events was used in the development of models. The data of past landslide events (locations) was randomly divided into a 70/30 ratio for training (70%) and validation (30%) of the models. Standard statistical measures, namely, accuracy (ACC), specificity (SPF), sensitivity (SST), positive predictive value (PPV), negative predictive value (NPV), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), were used to evaluate the performance of the models. Results indicated that the performance of all the models is very good (AUC > 0.90) and that of the LR model is the best (AUC = 0.926). Therefore, these single ML models can be used for the development of accurate landslide susceptibility maps. Our study demonstrated that the single models which are easy to use and can compete with the complex ensemble/hybrid models can be applied for landslide susceptibility mapping in landslide-prone areas.
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