Vietnam National University, Hanoi

www.vnu.edu.vn
Hanoi, Vietnam

Vietnam National University, Hanoi is a university in Hanoi, the capital of Vietnam.The university has 10 colleges and faculties. This is one of two national universities and also one of the two largest universities in Vietnam, along with Vietnam National University, Ho Chi Minh City. Wikipedia.

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Grant
Agency: European Commission | Branch: FP7 | Program: CSA-CA | Phase: ENV.2008.3.3.1.1. | Award Amount: 1.20M | Year: 2009

Many potentially hazardous compounds are traded as chemicals or incorporated as additives in products. Their release to the environment has been a concern of EC, UNO, WHO and OECD. The discussion of the assessment and management of chemicals and products led to the OECD program Globally Harmonised System of Classification and Labelling of Chemicals (GHS). The World Summit encouraged countries to implement GHS with a view of having the system operating by 2008. The need to form GHS on a global scale is part of EU policy. GHS aims to have the same criteria worldwide to classify the responsible trade and handling of chemicals and at the same time protect human health. The EU will ensure transition from the current EU Classification & Labelling (C\L) to the GHS which harmonizes with REACH. Countries like Japan and the USA announced to implement GHS in the near future. UNITAR supports other countries. However, a complete picture on the global state of implementation is not available. With the growing level of worldwide trade we however face unsafe products on the marked. Only last year reports about toys releasing hazardous components made it to headlines. Vietnam reported that all kind of plastic gets recycled and sold back to the market. This shows that global trade in a circular economy is not acceptable without globally agreed assessment methods and harmonised C\L. A ECB study revealed that the EU regulation REACH will require 3.9 mill. additional test animals if no alternative methods are accepted. The number of additional tests are unknown when GHS is implemented in a global scale. The CA RISKCYCLE will include experts from OECD, UNEP, SusChem, country experts from Asia, America and Europe. The overall objective of the project is to define with international experts future needs of R\D contributions for innovations in the field of risk-based management of chemicals and products in a global perspective using alternative testing strategies to minimize animal tests.


Horby P.,Vietnam National University, Hanoi
American Journal of Epidemiology | Year: 2012

Prospective community-based studies have provided fundamental insights into the epidemiology of influenza in temperate regions, but few comparable studies have been undertaken in the tropics. The authors conducted prospective influenza surveillance and intermittent seroprevalence surveys in a household-based cohort in Vietnam between December 2007 and April 2010, resulting in 1,793 person-seasons of influenza surveillance. Age-and sex-standardized estimates of the risk of acquiring any influenza infection per season in persons 5 years of age or older were 21.1% (95% confidence interval: 17.4, 24.7) in season 1, 26.4% (95% confidence interval: 22.6, 30.2) in season 2, and 17.0% (95% confidence interval: 13.6, 20.4) in season 3. Some individuals experienced multiple episodes of infection with different influenza types/subtypes in the same season (n = 27) or reinfection with the same subtype in different seasons (n = 22). The highest risk of influenza infection was in persons 5-9 years old, in whom the risk of influenza infection per season was 41.8%. Although the highest infection risk was in school-aged children, there were important heterogeneities in the age of infection by subtype and season. These heterogeneities could influence the impact of school closure and childhood vaccination on influenza transmission in tropical areas, such as Vietnam. © The Author 2012. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.2012This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. © The Author 2012. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.


Thao N.P.,Vietnam National University, Hanoi | Tran L.-S.P.,RIKEN
Critical Reviews in Biotechnology | Year: 2012

Soybean (Glycine max) is one of the most important crops in legume family. Soybean and soybean-based products are also considered as popular food for human and animal husbandry. With its high oil content, soybean has become a potential resource for the production of renewable fuel. However, soybean is considered one of the most drought-sensitive crops, with approximately 40% reduction of the yield in the worst years. Recent research progresses in elucidation of biochemical, morphological and physiological responses as well as molecular mechanisms of plant adaptation to drought stress in model plants have provided a solid foundation for translational genomics of soybean toward drought tolerance. In this review, we will summarize the recent advances in development of drought-tolerant soybean cultivars by gene transfer. © 2012 Informa Healthcare USA, Inc.


Dung D.,Vietnam National University, Hanoi
Foundations of Computational Mathematics | Year: 2015

Let (Formula presented.) be a set of n points in the d-cube (Formula presented.), and (Formula presented.) a family of n functions on (Formula presented.). We consider the approximate recovery of functions f on (Formula presented.) from the sampled values (Formula presented.), by the linear sampling algorithm (Formula presented.) The error of sampling recovery is measured in the norm of the space (Formula presented.)-norm or the energy quasi-norm of the isotropic Sobolev space (Formula presented.) for (Formula presented.) and (Formula presented.). Functions f to be recovered are from the unit ball in Besov-type spaces of an anisotropic smoothness, in particular, spaces (Formula presented.) of a “hybrid” of mixed smoothness (Formula presented.) and isotropic smoothness (Formula presented.), and spaces (Formula presented.) of a nonuniform mixed smoothness (Formula presented.). We constructed asymptotically optimal linear sampling algorithms (Formula presented.) on special sparse grids (Formula presented.) and a family (Formula presented.) of linear combinations of integer or half integer translated dilations of tensor products of B-splines. We computed the asymptotic order of the error of the optimal recovery. This construction is based on B-spline quasi-interpolation representations of functions in (Formula presented.) and (Formula presented.). As consequences, we obtained the asymptotic order of optimal cubature formulas for numerical integration of functions from the unit ball of these Besov-type spaces. © 2015 SFoCM


Duc N.D.,Vietnam National University, Hanoi
Composite Structures | Year: 2013

This paper presents an analytical investigation on the nonlinear dynamic response of eccentrically stiffened functionally graded double curved shallow shells resting on elastic foundations and being subjected to axial compressive load and transverse load. The formulations are based on the classical shell theory taking into account geometrical nonlinearity, initial geometrical imperfection and the Lekhnitsky smeared stiffeners technique with Pasternak type elastic foundation. The non-linear equations are solved by the Runge-Kutta and Bubnov-Galerkin methods. Obtained results show effects of material and geometrical properties, elastic foundation and imperfection on the dynamical response of reinforced FGM shallow shells. Some numerical results are given and compared with ones of other authors. © 2012 Elsevier Ltd.


Son L.H.,Vietnam National University, Hanoi
Applied Soft Computing Journal | Year: 2014

Geo-Demographic Analysis, which is one of the most interesting inter-disciplinary research topics between Geographic Information Systems and Data Mining, plays a very important role in policies decision, population migration and services distribution. Among some soft computing methods used for this problem, clustering is the most popular one because it has many advantages in comparison with the rests such as the fast processing time, the quality of results and the used memory space. Nonetheless, the state-of-the-art clustering algorithm namely FGWC has low clustering quality since it was constructed on the basis of traditional fuzzy sets. In this paper, we will present a novel interval type-2 fuzzy clustering algorithm deployed in an extension of the traditional fuzzy sets namely Interval Type-2 Fuzzy Sets to enhance the clustering quality of FGWC. Some additional techniques such as the interval context variable, Particle Swarm Optimization and the parallel computing are attached to speed up the algorithm. The experimental evaluation through various case studies shows that the proposed method obtains better clustering quality than some best-known ones. © 2014 Elsevier B.V.


Municipal Solid Waste (MSW) is an increasing concern at any municipality in the world, and is one of the primary factors that contribute greatly to the rising of climate change and global warming. MSW collection and disposal especially in the context of developing countries are indeed the urgent requirements for the sustainable development of environment and landscape, which rule over the quality-of-life and life expectancy of human being. In this paper, we concentrate on MSW collection at Danang city, which is one of four largest municipalities in Vietnam having high quantity of the average waste load per person and is bearing negative impacts of climate change such as severe weather conditions and natural disasters as a result. A novel vehicle routing model for the MSW collection problem at Danang city is presented. A novel hybrid method between Chaotic Particle Swarm Optimization and ArcGIS is proposed to generate optimal solutions from the vehicle routing model of Danang. Experimental results on the real dataset of Danang show that the proposed hybrid method obtains better total collected waste quantity than the relevant ones including the manual MSW collection procedure that is currently applied at this city. © 2014 Elsevier Ltd. All rights reserved.


Son L.H.,Vietnam National University, Hanoi
Expert Systems with Applications | Year: 2014

Recommender Systems (RS) have been being captured a great attraction of researchers by their applications in various interdisciplinary fields. Fuzzy Recommender Systems (FRS) is an extension of RS with the fuzzy similarity being calculated based on the users' demographic data instead of the hard user-based degree. Based upon the observations that the FRS researches did not offer a mathematical definition of FRS accompanied with its algebraic operations and properties, and the fuzzy similarity degree is not enough to express accurately the analogousness between users, in this paper we will present a systematic mathematical definition of FRS including theoretical analyses of algebraic operations and properties and propose a novel hybrid user-based fuzzy collaborative filtering method that integrates the fuzzy similarity degrees between users based on the demographic data with the hard user-based degrees calculated from the rating histories into the final similarity degrees in order to obtain high accuracy of prediction. Experimental results on some benchmark datasets show that the proposed method obtains better accuracy than other relevant methods. Lastly, an application for the football results prediction is given to illustrate the uses of the proposed method. © 2014 Elsevier Ltd. All rights reserved.


Grant
Agency: European Commission | Branch: FP7 | Program: ERC-AG | Phase: ERC-AG-PE10 | Award Amount: 1.62M | Year: 2014

More than 100 million people living on the floodplains of the Ganges-Brahmaputra-Meghna, Mekong and Red River, all draining the Himalayas, are consuming arsenic contaminated water. Providing safe drinking water for these people requires a quantitative understanding of the processes regulating the groundwater arsenic content and this knowledge is presently not available. In PREDIAS we propose a revolutionary new approach to study these arsenic contaminated aquifers where sediments and groundwaters are considered as one reacting unit that is changing over time. The key hypothesis is that it is the aquifer sediment burial age that is the overall controlling parameter for the arsenic content. This new approach is explored by studying the groundwater chemistry as a function of sediment burial age, which is equivalent to the geological evolution over time, in part of the Red River floodplain in Vietnam. The investigations comprise delineating the sedimentological development over the last 9000 yrs as well as reconstructing hydrogeological conditions over that period. Process studies will reveal the effect of burial age on the chemical properties of the sediments and the arsenic release mechanisms. They comprise the binding and release mechanisms of arsenic to the aquifer sediment, and the reactivity of sedimentary organic carbon and iron oxides which drive the redox reactions controlling the water chemistry and arsenic mobilization. Information on the sedimentological and hydrogeological development over time as well as a quantification of the geochemical processes will be incorporated in a 3-D reactive transport model which over the last 9000 years, in steps of about 1000 years, can predict the evolution of the arsenic content over space and time in the groundwater of the studied area. The model can be extended in a more conceptual form to larger parts of the Red River delta and Bangladesh using satellite imaging to reveal the geological development in those areas.


Son L.H.,Vietnam National University, Hanoi
Expert Systems with Applications | Year: 2015

Fuzzy clustering is considered as an important tool in pattern recognition and knowledge discovery from a database; thus has been being applied broadly to various practical problems. Recent advances in data organization and processing such as the cloud computing technology which are suitable for the management, privacy and storing big datasets have made a significant breakthrough to information sciences and to the enhancement of the efficiency of fuzzy clustering. Distributed fuzzy clustering is an efficient mining technique that adapts the traditional fuzzy clustering with a new storage behavior where parts of the dataset are stored in different sites instead of the centralized main site. Some distributed fuzzy clustering algorithms were presented including the most effective one - the CDFCM of Zhou et al. (2013). Based upon the observation that the communication cost and the quality of results in CDFCM could be ameliorated through the integration of a distributed picture fuzzy clustering with the facilitator model, in this paper we will present a novel distributed picture fuzzy clustering method on picture fuzzy sets so-called DPFCM. Experimental results on various datasets show that the clustering quality of DPFCM is better than those of CDFCM and relevant algorithms. © 2014 Elsevier B.V. All rights reserved.

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