| 1. |
- Lozano, Rafael, et al.
(författare)
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Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017
- 2018
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Ingår i: The Lancet. - : Elsevier. - 1474-547X .- 0140-6736. ; 392:10159, s. 2091-2138
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Tidskriftsartikel (refereegranskat)abstract
- Background: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings: The global median health-related SDG index in 2017 was 59·4 (IQR 35·4–67·3), ranging from a low of 11·6 (95% uncertainty interval 9·6–14·0) to a high of 84·9 (83·1–86·7). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation: The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030.
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| 2. |
- Pham, Binh Thai, et al.
(författare)
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Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction
- 2020
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Ingår i: Symmetry. - Switzerland : MDPI. - 2073-8994. ; 12:6
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Tidskriftsartikel (refereegranskat)abstract
- Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.
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| 3. |
- 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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| 4. |
- Nguyen, Hai Nam, et al.
(författare)
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A Blockchain-based SDN East/West Interface
- 2022
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Ingår i: 2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022). - : IEEE. - 9781665435406 - 9781665435413 ; , s. 5759-5764
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Konferensbidrag (refereegranskat)abstract
- Software-Defined Networking (SDN) architecture was developed to address the shortcomings of traditional network architectures. It allows system administrators to easily manage and configure the network by separating and abstracting the control plane from the data plane. All the knowledge and intelligence of SDN is concentrated in a software entity called the SDN controller, making the network programmable. However, a large-scale SDN architecture, particularly in the IoT domain, requires the implementation of a physically distributed control mechanism. Such a mechanism, based on the East/West interface raises many challenges in terms of scalability, reliability, security, consistency, and traceability. The development of the Blockchain allows addressing some of these challenges. In this paper, we present a design using Blockchain technology to improve SDNs in terms of trackability and discuss the adaptations required for large-scale deployment. Experimental results clearly show that the use of a proof-of-authority consensus algorithm in combination with a Merkle tree approach reduces the impact in terms of latency as well as in terms of Gas consumption.
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| 5. |
- Nguyen, Hai Nam, et al.
(författare)
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A survey of Blockchain technologies applied to software-defined networking : Research challenges and solutions
- 2021
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Ingår i: IET Wireless Sensor Systems. - : Wiley. - 2043-6386 .- 2043-6394. ; 11:6, s. 233-247
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Forskningsöversikt (refereegranskat)abstract
- Software-Defined Networking (SDN) brought a groundbreaking idea to facilitate network system management by decoupling and abstracting the Control plane and Data plane of traditional networks. The centralised control offers network administrators many benefits such as a global view of the network, programmability, dynamic updating of forwarding rules, and software-based traffic analysis. The SDN architecture has been applied a lot in practice, and especially in Internet of Things (IoT) platforms. With the superiority of SDN, IoT devices can be managed and configured much more easily when combined. However, SDN also raises many challenges in terms of scalability, reliability, and security. Blockchain is another promising solution for secure information storage and transmission technology that operates without a centralised authority. Applying Blockchain technology into SDN can address some of the current issues of SDN by providing decentralised methods to authenticate exchanged network information. This study provides a comprehensive survey on Blockchain technologies applied to SDN in both security and non-security fields. First, related studies and an overview of SDN and the background of Blockchain technology are presented. Then, the authors review how Blockchain technologies are applied in SDN from two perspectives: non-security and security-aware approaches. Finally, challenges and broader perspectives are discussed.
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| 6. |
- Nguyen-Tien, Thang, et al.
(författare)
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The Distribution and Composition of Vector Abundance in Hanoi City, Vietnam : Association with Livestock Keeping and Flavivirus Detection
- 2021
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Ingår i: Viruses. - : MDPI. - 1999-4915. ; 13:11
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Tidskriftsartikel (refereegranskat)abstract
- Background: Dengue virus and Japanese encephalitis virus are two common flaviviruses that are spread widely by Aedes and Culex mosquitoes. Livestock keeping is vital for cities; however, it can pose the risk of increasing the mosquito population. Our study explored how livestock keeping in and around a large city is associated with the presence of mosquitoes and the risk of them spreading flaviviruses.Methods: An entomological study was conducted in 6 districts with 233 households with livestock, and 280 households without livestock, in Hanoi city. BG-Sentinel traps and CDC light traps were used to collect mosquitoes close to animal farms and human habitats. Adult mosquitoes were counted, identified to species level, and grouped into 385 pools, which were screened for flaviviruses using a pan-flavivirus qPCR protocol and sequencing.Results: A total of 12,861 adult mosquitoes were collected at the 513 households, with 5 different genera collected, of which the Culex genus was the most abundant. Our study found that there was a positive association between livestock keeping and the size of the mosquito population-most predominantly between pig rearing and Culex species (p < 0.001). One pool of Cx. tritaeniorhynchus, collected in a peri-urban district, was found to be positive for Japanese encephalitis virus.Conclusions: The risk of flavivirus transmission in urban areas of Hanoi city due to the spread of Culex and Aedes mosquitoes could be facilitated by livestock keeping.
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| 7. |
- Pham, Binh Thai, et al.
(författare)
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A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping
- 2020
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Ingår i: Water. - Switzerland : MDPI. - 2073-4441. ; 12:1, s. 1-21
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Tidskriftsartikel (refereegranskat)abstract
- Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.
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| 8. |
- Stanaway, Jeffrey D., et al.
(författare)
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Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017
- 2018
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Ingår i: The Lancet. - 1474-547X .- 0140-6736. ; 392:10159, s. 1923-1994
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Tidskriftsartikel (refereegranskat)abstract
- Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk-outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk-outcome pairs, and new data on risk exposure levels and risk- outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017.
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| 9. |
- Trinh, Duc Anh, et al.
(författare)
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Impact of terrestrial runoff on organic matter, trophic state, and phytoplankton in a tropical, upland reservoir
- 2016
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Ingår i: Aquatic Sciences. - : Springer Science and Business Media LLC. - 1015-1621 .- 1420-9055. ; 78:2, s. 367-379
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Tidskriftsartikel (refereegranskat)abstract
- The impact of organic matter inputs from agricultural, forest and domestic sources on aquatic processes has been considerably less studied in tropical reservoirs relative to temperate systems despite the high number of these small aquatic systems in the tropics. Here we present the results of an in situ mesocosm study that examined the impact of allochthonous organic matter on a headwater reservoir in Northern Vietnam. We examined the impact of wastewater and soils from floodplain paddies, Acacia mangium plantations and from upland slopes on the metabolic status of the reservoir. The addition of floodplain paddy soils to the reservoir water led to a rapid switch in metabolic status from net autotrophic to net heterotrophic. In contrast, the addition of wastewater in low concentrations had less impact on the metabolic status of the reservoir, reflecting the low population density in the area. The addition of floodplain paddy soils also increased phytoplankton diversity and evenness relative to the control. In summary, soils from floodplain paddies and from A. mangium plantations had the highest impact on the reservoir, with upland soils and wastewater having less of an impact. We also found that primary production in this reservoir was nitrogen limited. In order to avoid accelerating the impact of runoff on the reservoir, future management options should perhaps focus on minimizing water and sediment runoff from upstream paddy fields and from A. mangium plantations. These results also underline the importance of studying these upland tropical water bodies that can contribute an important but, on the whole, ignored part of the global carbon balance.
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| 10. |
- Berglund, Björn, et al.
(författare)
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Clonal spread of carbapenem-resistant Klebsiella pneumoniae among patients at admission and discharge at a Vietnamese neonatal intensive care unit
- 2021
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Ingår i: Antimicrobial Resistance and Infection Control. - : BMC. - 2047-2994. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Background The increasing prevalence of carbapenem-resistant Enterobacteriaceae (CRE) is a growing problem globally, particularly in low- to middle-income countries (LMICs). Previous studies have shown high rates of CRE colonisation among patients at hospitals in LMICs, with increased risk of hospital-acquired infections. Methods We isolated carbapenem-resistant Klebsiella pneumoniae (CRKP) from faecal samples collected in 2017 from patients at admission and discharge at a Vietnamese neonatal intensive care unit (NICU). 126 CRKP were whole-genome sequenced. The phylogenetic relationship between the isolates and between clinical CRKP isolates collected in 2012-2018 at the same hospital were investigated. Results NDM-type carbapenemase-(61%) and KPC-2-encoding genes (41%) were the most common carbapenem resistance genes observed among the admission and discharge isolates. Most isolates (56%) belonged to three distinct clonal clusters of ST15, carrying bla(KPC-2), bla(NDM-1) and bla(NDM-4), respectively. Each cluster also comprised clinical isolates from blood collected at the study hospital. The most dominant ST15 clone was shown to be related to isolates collected from the same hospital as far back as in 2012. Conclusions Highly resistant CRKP were found colonising admission and discharge patients at a Vietnamese NICU, emphasising the importance of continued monitoring. Whole-genome sequencing revealed a population of CRKP consisting mostly of ST15 isolates in three clonally related clusters, each related to blood isolates collected from the same hospital. Furthermore, clinical isolates collected from previous years (dating back to 2012) were shown to likely be clonally descended from ST15 isolates in the largest cluster, suggesting a successful hospital strain which can colonise inpatients.
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| 11. |
- Grüning, Björn, et al.
(författare)
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Bioconda: A sustainable and comprehensive software distribution for the life sciences
- 2017
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multi-platform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges.
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| 12. |
- 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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| 13. |
- Hien Tran, Thi, et al.
(författare)
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Triolein from Coix lacryma-jobi induces cell cycle arrest through p53/p21 signaling pathway
- 2016
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Ingår i: Biomedical and Pharmacology Journal. - : Oriental Scientific Publishing Company. - 0974-6242. ; 9:2, s. 519-524
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Tidskriftsartikel (refereegranskat)abstract
- p53, a tumor suppressor protein, has important roles in DNA repair, cell cycle and apoptosis, is a one of the key events in cancer development. Coix lacryma-jobi seed has been used as a food and traditional medicine plant with anti-oxidant, anti-cancer and anti-diabetic effects. In currently research, we identified the most potent p53-increasing compound among 4 compounds (1-4) found in Coix lacryma-jobi and demonstrated its molecular mechanism in MCF-7 cells. Among the four isolated compounds (1-4), triolein most increased p53. Triolein treatment induced p53, p21, p27 and Bax in MCF-7 cells. Moreover, triolein caused S phase arrest through suppression of CDK1, phopho-Rb and E2F1 in dose-dependent manner. We also observed the decreasing of DNA synthesis by triolein. These data suggest that triolein may induced cell cycle restart involve DNA synthesis and apoptosis pathway in MCF-7 cells.
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| 14. |
- Jakobsen, Frida, et al.
(författare)
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Urban livestock-keeping and dengue in urban and peri-urban Hanoi, Vietnam.
- 2019
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Ingår i: PLoS Neglected Tropical Diseases. - : Public Library of Science (PLoS). - 1935-2727 .- 1935-2735. ; 13:11
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Tidskriftsartikel (refereegranskat)abstract
- Urban livestock provides an important source of food and income, but it may increase the risks for disease transmission. Vectors, such as mosquitoes, might increase and thereby cause an enhanced transmission of infectious diseases, such as dengue fever; considered the most important mosquito-borne viral disease globally. This cross-sectional study evaluated the awareness of dengue fever and investigated how the presence of dengue vectors is affected by the keeping of livestock in urban households in the city of Hanoi, Vietnam. From February to March 2018, during the season of lowest occurrence of dengue in Hanoi, 140 households were interviewed, of which 69 kept livestock. A general trend was observed; respondents living in the Dan Phuong district, a peri-urban district, had better knowledge and practice regarding dengue as compared to the urban Ha Dong district. In total, 3899 mosquitoes were collected and identified, of which 52 (1.33%) were Aedes species. A significant difference between the two districts was observed, with more households in Ha Dong having Aedes spp. mosquitoes (p = 0.02) and a higher incidence of dengue fever (p = 0.001). There was no significant association between livestock-rearing and the presence of Aedes spp. mosquitoes (p = 0.955), or between livestock-rearing and the incidence of dengue fever (p = 0.08). In conclusion, this study could not find any indication that households keeping livestock were at higher risk of dengue virus infections in Hanoi during the season of lowest occurrence of dengue, but clearly indicated the need of more information provided to urban inhabitants, particularly on personal protection.
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| 15. |
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| 16. |
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| 17. |
- Menkveld, Albert J., et al.
(författare)
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Nonstandard Errors
- 2024
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Ingår i: JOURNAL OF FINANCE. - : Wiley-Blackwell. - 0022-1082 .- 1540-6261. ; 79:3, s. 2339-2390
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Tidskriftsartikel (refereegranskat)abstract
- In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
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| 18. |
- Morikawa, Kyojiro, et al.
(författare)
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Fused silica microchannel fabrication with smooth surface and high etching selectivity
- 2023
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Ingår i: Journal of Micromechanics and Microengineering. - : IOP Publishing. - 0960-1317 .- 1361-6439. ; 33:4
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Tidskriftsartikel (refereegranskat)abstract
- Channel fabrication technology has become increasingly important for microfluidic and nanofluidic devices. In particular, glass channels have high chemical and physical stability, high optical transparency, and ease of surface modification, so that there is increasing interest in glass microfluidic devices for chemical experiments in microfluidics and nanofluidics. For the fabrication of glass channels, especially those with a high aspect ratio (depth/width), lithography using a metal resist and dry etching have mainly been used. However, there are still issues involving the surface roughness of the etched channel and the low etching selectivity. In this study, a microchannel fabrication method with high etching selectivity that produces a smooth etched surface was developed. First, interference during dry etching by remaining Cr particles after the photolithography and Cr etching processes was assumed as the cause of the rough etched surface. Three different dry etching processes were introduced to verify this. In process 1 without removal of the Cr particles, the etched surface was not flat and had a 1 μm scale roughness. In process 2 where a cleaning process was included and high power etching was conducted, a smooth surface with a 1 nm scale roughness and a faster etching rate of 0.3 μm min−1 were obtained. For this high-power etching condition, the etching selectivity (fused silica/Cr) was relatively low at approximately 39-43. In process 3 with a cleaning process and low-power etching, although the etching rate was relatively low at 0.1 μm min−1, a smooth surface with 1 nm scale roughness (10 nm scale roughness deeper than 40 μm in the depth region) and a much higher etching selectivity of approximately 79-84 were obtained. The dry etching method presented in this study represents a significant contribution to microfluidics/nanofluidics for microchannel/nanochannel fabrication.
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| 19. |
- 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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| 20. |
- 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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| 21. |
- 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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| 22. |
- Nguyen, Quang Hung, et al.
(författare)
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Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
- 2021
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Ingår i: Mathematical problems in engineering (Print). - UK : Hindawi Publishing Corporation. - 1024-123X .- 1563-5147. ; 2021, s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive capability of the models under different training and testing ratios. Besides, Monte Carlo simulation was simultaneously carried out to evaluate the performance of the proposed models, taking into account the random sampling effect. The results showed that although all three ML models performed well, the ANN was the most accurate and statistically stable model after 1000 Monte Carlo simulations (Mean R = 0.9348) compared with other models such as Boosted (Mean R = 0.9192) and ELM (Mean R = 0.8703). Investigation on the performance of the models showed that the predictive capability of the ML models was greatly affected by the training/testing ratios, where the 70/30 one presented the best performance of the models. Concisely, the results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.
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| 23. |
- Nguyen, Thi Ngoc Phuong, 1993, et al.
(författare)
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Individual-, social- and policy- factors associated with smoking cessation among adult male cigarette smokers in Hanoi, Vietnam: a longitudinal study
- 2023
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Ingår i: BMC PUBLIC HEALTH. - : BioMed Central (BMC). - 1471-2458. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundNearly one-in-two Vietnamese men smoke cigarettes placing them among the highest tobacco consumers in the world. Despite the need for smoking cessation to curb the burden of tobacco-related diseases in Vietnam, this rate remains at less than 30%. Therefore, this study examines individual-, social- and policy factors associated with smoking cessation among adult male smokers in Vietnam.MethodsWe established a longitudinal International Tobacco Control study of male smokers in Hanoi, Vietnam, in September 2018. This paper analyses 1525 men who participated in baseline and one-year follow-up. We applied a weighted multivariable logistic regression to examine the association between smoking cessation and individual-, social- and policy predictors.ResultsAt follow-up, 14.8% of participants had quit smoking for at least 30 consecutive days during the last year. Among the persistent smokers, 56.6% expressed intention to quit smoking. Factors associated with smoking cessation included a lower number of cigarettes smoked per day (aOR = 0.96, 95% CI: 0.94, 0.99) and having several attempts to quit smoking (aOR = 2.16, 95% CI 1.13, 4.12). Intention to quit smoking was associated with multiple quit attempts, a chronic condition diagnosis, more tobacco-related knowledge, greater self-efficacy, and more worries about their future health. The perceived impact of smoke-free policy and health warning labels were positively associated with intention to quit at any stage.ConclusionsInterventions aimed at increasing smoking cessation should focus on all aspects of individual, social, and policy factors. Persistent smokers are more motivated to quit if they have made multiple quit attempts, more self-efficacy of quitting and worried about their future health, indicating that increasing smokers' beliefs and knowledge may be important for behavioural change. Health warning labels and tobacco taxation policies should be maintained and promoted as they are perceived to be particularly useful for persistent smokers' intention to quit.
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| 24. |
- Nguyen-Tien, Thang, et al.
(författare)
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Knowledge and practice on prevention of mosquito-borne diseases in livestock-keeping and non-livestock-keeping communities in Hanoi city, Vietnam : A mixed-method study
- 2021
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 16:2
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Tidskriftsartikel (refereegranskat)abstract
- Mosquito-borne diseases (MBDs) are causing high morbidity and mortality for humans. Urban livestock keeping is still common in cities around the world. The animals may serve as reservoirs for zoonotic MBDs, which increase the risks for humans. Here we assess the knowledge and practices related to MBDs in households with livestock and without livestock and explore the perceptions of the health care sector about MBDs and livestock keeping in Hanoi city of Vietnam in a cross-sectional study. A quantitative survey was conducted including 513 households with and without livestock-keeping in six districts and complemented with qualitative surveys with four health staff from Hanoi Center of Disease Control and three district health centers. The quantitative survey indicated that the participants possessed basic knowledge on MBDs with an average score of 18.3 out of 35, of which non-livestock-keeping households had a better knowledge than households keeping livestock (p<0.05). Both household categories had low score, 3.5 out of 11, regarding preventive practices against MBDs. The negative binomial model showed that occupation and location of living were factors associated to the knowledge on MBDs. Farmers were likely to have better preventive practices as compared to office workers (p<0.05). Those who had better knowledge also had more adequate preventive practices against MBDs (p<0.001). The qualitative survey revealed that livestock keeping was determined as increasing risks of MBDs due to the increase of mosquito population. It is recommended that community campaigns to raise the awareness and change behavior on MBDs should be organized based on collaboration between the health sector and the veterinary sector for households with and without livestock living in central urban and peri-urban areas. Further studies are needed to confirm the association between urban livestock keeping and potential increasing risks of MBDs such as dengue and Japanese encephalitis.
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| 25. |
- Pham, Binh Thai, et al.
(författare)
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Extreme learning machine based prediction of soil shear strength : A sensitivity analysis using Monte Carlo simulations and feature backward elimination
- 2020
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 12:6, s. 1-29
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Tidskriftsartikel (refereegranskat)abstract
- Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In this study, the main objective is to analyze the sensitivity of an advanced ML method, namely the Extreme Learning Machine (ELM) algorithm under different feature selection scenarios for prediction of shear strength of soil. Feature backward elimination supported by Monte Carlo simulations was applied to evaluate the importance of factors used for the modeling. A database constructed from 538 samples collected from Long Phu 1 power plant project was used for analysis. Well-known statistical indicators, such as the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE), were utilized to evaluate the performance of the ELM algorithm. In each elimination step, the majority vote based on six elimination indicators was selected to decide the variable to be excluded. A number of 30,000 simulations were conducted to find out the most relevant variables in predicting the shear strength of soil using ELM. The results show that the performance of ELM is good but very different under different combinations of input factors. The moisture content, liquid limit, and plastic limit were found as the most critical variables for the prediction of shear strength of soil using the ML model.
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