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International audience; According to recent studies, Vietnam is one of the twenty countries most affected by natural disasters in the world, and particularly by floods either on the low elevation coastal zones (risk of submersion) or along the Red River and the Mekong River (risk of flooding). In this context, dams are both means of mitigation but also threats given the possible failures and ruptures. The authorities must, therefore, prepare warning systems and evacuation plans for the downstream population to avoid loss of life. Agent-based models are now the approach of choice to support such preparedness by considering the system as a whole and integrating dynamics of different natures: hydrology, population behavior, evacuation, crisis management, etc. To design such a decision-support tool, modelers generally need to couple different formalisms, such as diffusion equations when considering the hydrodynamic part, and agent-based modeling when considering inhabitants’ behaviors. This is the goal of the ESCAPE project, which uses agent-based simulations to explore evacuation strategies and contribute to the development and evaluation of evacuation plans. In this study, to improve the ESCAPE framework, we propose to combine a hydraulic dam failure model with an agent-based evacuation model using the GAMA platform. We focus on the evacuation of a Hanoi city (Vietnam) district, threatened by flooding due to the failure of the Hoa Binh dam located more than 80 km upstream of the city. We demonstrate how to methodologically and operationally couple a hydrodynamic water diffusion model (implemented using the HEC-RAS software) and a multi-paradigm evacuation model (using the ESCAPE framework). Our goal is to extend and enrich this population evacuation model by coupling it with flood simulation.

International audience; Irrigation systems contribute worldwide to the provision of a wide range of services on which the survival and well-being of humanity depend. They are of primary importance in Vietnam where about 90% of the water used is for irrigation and aquaculture and where agriculture is the largest employer and a major contributor to the national GDP and to the income of the low-salary households. Nevertheless, irrigation systems have recently been subjected to several issues including increasing demand, pollution, under-investment, depletion of resources or environmental changes. Any mitigation measure against these issues needs to be sustainable with respect to the very diverse uses of the water, the changing conditions upstream and downstream, and the somewhat conflicting objectives carried out by land-use/agricultural planning on one hand and urbanization and society well-being on the other. This need of sustainability requires the design of innovative tools to tackle these issues. This work aims at exploring the usage of Agent-Based Modelling coupled with a tangible and interactive interface in order to enhance interactions between stakeholders and support the evaluation of various alternatives of the management of the Bac Hung Hai irrigation system.

We investigate a system of two species exploiting a common resource. We consider both abiotic (i.e. with a constant resource supply rate) and biotic (i.e. with resource reproduction and self-limitation) resources. We are interested in the asymmetric competition where a given consumer is the locally superior resource exploiter (LSE) and the other is the locally inferior resource exploiter (LIE). They also interact directly via interference competition in the sense that LIE individuals can use two opposite strategies to compete with LSE individuals: we assume, in the first case, that LIE uses an avoiding strategy, i.e. LIE individuals go to a non-competition patch to avoids competition with LSE individuals, and in the second one, LIE uses an aggressive strategy, i.e. being very aggressive so that LSE individuals have to go to a non-competition patch. We further assume that there is no resource in the non-competition patch so that individuals have to come back to the competition patch for their maintenance, and the migration process acts on a fast time scale in comparison with demography and competition processes. The models show that being aggressive is efficient for LIE's survival and even provoke global extinction of the LSE and this result does not depend on the nature of resource.

The main goal of medicine research is to precisely provide treatment for each patient. In particular, clinicians want to use right drugs and doses for each patient according to their biological characteristics to maximize treatment's efficiency. With support of high-throughput technologies, a large amount of -omics and drug response data has been generated for human's tumors and cell lines. This wealth data facilitates the research for predicting drug response for each patient before selecting drugs for intake. Many computational methods have been also proposed for this purpose using -omics data of cell lines to predict the response on untested drugs. Previous methods have assumed a linear/non-linear relationship between -omics data and drug response for individual drugs. However, it should be noted that drugs can be functionally related to others since they may share some chemical structures. In addition, clinicians are also interested in knowing which biomarkers affect the response. Therefore, in this study, we assumed a linear relation and proposed a method based on multi-task regression learning to predict response for not only one but a panel of drugs. Comparing with previous single-task linear regression method, we found that our method achieves better performance in terms of correlation coefficient for major number of drugs.

Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarities between drugs and between diseases are usually used as inputs. In addition, known drug-disease associations are also needed for the methods as prior information. It should be noted that those associations are still not well established due to the fact that many of marketed drugs have been withdrawn and this could affect the outcome of the methods. In this study, we propose a novel method named RLSDR (Regularized Least Square for Drug Repositioning) to find new uses of drugs. More specifically, it relies on a semi-supervised learning model, Regularized Least Square, thus it does not require definition of non-drug-disease associations as previously proposed machine learning-based methods. In addition, the similarity between drugs measured by chemical structures of drug compounds and the similarity between diseases which share phenotypes can be represented in a form of either similarity network or similarity matrix as inputs of the method. Moreover, instead of using a gold-standard set of known drug-disease associations, we construct an artificial set of the associations based on known disease-gene and drug-target associations. Experiment results demonstrate that RLSDR achieves better prediction performance on the artificial set of drug-disease associations than that on the gold-standard ones in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC). In addition, it outperforms two representative network-based methods irrespective of the prior information of drug-disease associations. Novel indications for a number of drugs are also identified and validated by evidences from a different data resource.

The majority of carbon on earth is in the form of soil organic matter. And its degradation by microorganisms leads to the remineralization of carbon as carbon dioxide. The microbial activity causes a reduction of soil carbon and increases atmospheric carbon. However, most models of organic matter do not explicitly take into account this reality. We try to answer these questions by developing and validating a model describing the action of microorganisms on degradation of organic matter. We use simulation domain as the pores in the soil modeled by a network of balls. The model is solved numerically in the balls by the finite element method with the solver of partial differential equations (PDEs) Freefem3d. We compare the numerical results with experimental data on the mineralization of soil carbon.

In this paper, we investigate effects of spatial heterogeneous environment and fast migration of individuals on the coexistence of the intraguild predation (IGP) dynamics. We present a two-patch model. We assume that on one patch two species compete for a common resource, and on the other patch one species can capture the other one for the maintenance. We also assume IGP individuals are able to migrate between the two patches and the migration process acts on a fast time scale in comparison with demography, predation and competition processes. We show that under certain conditions the heterogeneous environment and fast migration can lead to coexistence of the two species.

We present a mathematical model of two competing marine species that are harvested. We consider three models according to different levels of complexity, without and with species refuge and density-independent and density-dependent species movement between fishing area and refuge. We particularly study the effects of the fishing pressure on the outcome of the competition. We focus on conditions that allow an inferior competitor to invade as a result of fishing pressure. The model is discussed in relationship to the case of the thiof and the octopus along the Atlantic West African coast. At the origin, the thiof was abundant and the octopus scarce in that region. Since, the fishing pressure has strongly increased in some fishing areas leading to the depletion of the thiof and the invasion of its competitor, the octopus.

We presented a multi-scale model, by coupling of three different component models: individual based model (IBM), spatial moment-based model (SMBM) and equation-based model (EBM), for studying of spreading of infectious disease in a 3D office building. We proposed an up-scaling methodology to derive a SMBM from a given IBM, and to generate an EBM from the SMBM. Our results showed that the multi-scale model, by taking advantages of the three component models, is able to support different and complementary views of the spreading of infectious disease system. It is, therefore, can be seen as an effective model to describe disease spreading in a heterogeneous environment.

The amount of municipal solid waste (MSW) has been increasing steadily over the last decade by reason of population rising and waste generation rate. In most of the urban areas, disposal sites are usually located outside of the urban areas due to the scarcity of land. There is no fixed route map for transportation. The current waste collection and transportation are already overloaded arising from the lack of facilities and insufficient resources. In this paper, a model for optimizing municipal solid waste collection will be proposed. Firstly, the optimized plan is developed in a static context, and then it is integrated into a dynamic context using multi-agent based modelling and simulation. A case study related to Hagiang City, Vietnam, is presented to show the efficiency of the proposed model. From the optimized results, it has been found that the cost of the MSW collection is reduced by 11.3%.

The aim of this work is to investigate the effect of density-dependent dispersal on the outcome of competition in a heterogeneous environment. We present a classical spatial interspecific competition model with two patches connected by fast dispersal. We assume local pre-emptive competition, i.e. on each isolated patch, the species having the largest initial density would exclude the other one. We assume that individuals of species 1 disperse at constant dispersal rates, while species 2 dispersal is density dependent. Species 2 individuals are more likely to disperse when there are many species 1 competitors in a patch. We investigate if a dispersal strategy that aims at avoiding its competitor can be beneficial. Dispersal between patches is assumed to be faster than local population dynamics on each patch. We take advantage of these time scales in order to reduce the complete model into an aggregated model governing total densities of both species at a slow time scale. The analysis of this global model shows that using a density dependent strategy for dispersal is beneficial for species 2 in the sense that it prevents its own extinction and even it allows species 2 to exclude species 1 under some conditions.

The purpose of this paper is to investigate the effects of conflicting tactics of resource acquisition on stage structured population dynamics. We present a population subdivided into two distinct stages (immature and mature). We assume that immature individual survival is density dependent. We also assume that mature individuals acquire resources required to survive and reproduce by using two contrasted behavioral tactics (hawk versus dove). Mature individual survival thus is assumed to depend on the average cost of fights while individual fecundity depends on the average gain in the competition to access the resource. Our model includes two parts: a fast part that describes the encounters and fights involves a game dynamic model based upon the replicator equations, and a slow part that describes the long-term effects of conflicting tactics on the population dynamics. The existence of two time scales let us investigate the complete system from a reduced one, which describes the dynamics of the total immature and mature densities at the slow time scale. Our analysis shows that an increase in resource value may decrease total population density, because it promotes individual (i.e. selfish) behavior. Our results may therefore find practical implications in animal conservation or biological control for instance., Le but de cet article est d'étudier les effets de comportements agressifs dans l'acquisition de ressources sur la dynamique d'une population structurée en deux classes d'âge. Nous considérons une population divisée en deux sous populations distinctes (immatures et adultes matures). Nous supposons que la survie individuelle de la population immature est densité dépendante. Nous supposons également que les individus matures sont en compétition pour acquérir les ressources nécessaires pour leur survie et leur reproduction. Les adultes utilisent deux tactiques comportementales (faucon et colombe). Lors de confrontations entre adultes, la survie d'un individu mature est supposée être affectée par le coût moyen des combats alors que la fécondité dépend du gain moyen obtenu en accédant à la ressource. Notre modèle comprend deux parties : une partie rapide qui décrit les rencontres et les combats basée sur les équations du réplicateur, et une partie lente qui décrit les effets à long terme des tactiques conflictuelles sur la dynamique de la population. L'existence de deux échelles de temps nous permet d'étudier le système complet à partir d'un système réduit, qui décrit la dynamique des densités totales des immatures et des adultes à l'échelle de temps lente. Notre analyse montre que le taux de croissance global de la population dépend de la valeur de la ressource et du coût des combats entre adultes. Nos résultats trouvent des implications pratiques dans la conservation des espèces et le contrôle biologique.

Partial Differential Equations (PDEs) have been already widely used to simulate various complex phenomena in porous media. This paper is one of the first attempts to apply PDEs for simulating in real 3D structures. We apply this scheme to the specific case study of the microbial decomposition of organic matter in soil pore space. We got a 3D geometrical representation of the pore space relating to a network of volume primitives. A mesh of the pore space is then created by using the network. PDEs system is solved by free finite elements solver Freefem3d in the particular mesh. We validate our PDEs model to experimental data with 3D Computed Tomography (CT) images of soil samples. Regarding the current state of art on soil organic matter decay models, our approach allows taking into account precise 3D spatialization of the decomposition process by a pore space geometry description.

In this paper, we investigate the relationship between migration and species distribution in multizone environment. We present a discrete model for migration of three competing species over three zones. We prove that the migration tactics of species leads to the fact that the system exponentially converges to one of two typical configurations: the first one is a case where each zone contains only one species, the second one is a case where one species is of density 1 in one zone, another species stays and dominates in the two other zones, and the last species is evenly split into the 3 zones with a density one third in each. We also show a characterization of the initial conditions under which the system converges to one of the two configurations.

We investigate whether asymmetric fast migration can modify the predictions of classical competition theory and, in particular revert species dominance. We consider a model of two species competing for an implicit resource on a habitat divided into two patches. Both patches are connected through constant migration rates and in each patch local dynamics are driven by a Lotka-Volterra competition system.Local competition is asymmetric with the same superior competitor in both patches. Migration is asymmetric, species dependent and fast in comparison to local competitive interactions. The species and patches are taken to be otherwise similar in both patches we assume the same carrying capacities for both species, and the same growth rates and pair-wise competition coefficients for each species.We show that global dynamics can be described by a classical Lotka-Volterra competition model. We found that by modifying the ratio of intraspecific migration rates for both species all possible combinations of global species relative dominance can be achieved. We find specific conditions for which the local superior competitor is globally excluded. This is to our knowledge the first study showing that fast asymmetric migration can lead to inferior competitor dominance in a homogeneous environment. We conclude that disparity of temporal scales between migration and local dynamics may have important consequences for the maintenance of biodiversity in spatially structured populations. © 2010 Elsevier Ltd.

International audience; The development of permanent or temporary pedestrian areas, whether for leisure or to decrease air pollution, has become an integral part of urban planning in numerous cities around the world. Hanoi, the capital of Vietnam, began to implement its first area, around the iconic Hoan Kiem lake, a few years ago. In most of cases, however, road closure is likely to deport traffic to nearby neighbourhoods with the consequences of intensifying congestion and, possibly, increasing air pollution in these areas. Because this outcome might appear counter-intuitive to most stakeholders, it is becoming more and more necessary to analyse, assess and share the impacts of these developments in terms of traffic and pollution shifts before implementing them. In this project, we used the GAMA platform to build an agent-based model that simulates the traffic, its emissions of air pollutants, and the diffusion of these pollutants in the district of Hoan Kiem. This simulation has been designed so as to serve either as a decision support tool for local authorities or as an awareness-raising tool for the general public: thanks to its display on a physical 3D model of the district, people can effectively and naturally interact with it at public venues. Although more accurate data and more realistic diffusion models are necessary and will need further research in the future, the simulation is already able to reflect traffic and air pollution peaks during rush hours, allowing residents and developers to understand the impact of pedestrianization on air quality in different scenarios.