A modular modelling framework for hypotheses testing in the simulation of urbanisation

In this paper, we present a modelling experiment developed to study systems of cities and processes of urbanisation in large territories over long time spans. Building on geographical theories of urban evolution, we rely on agent-based models to 1/ formalise complementary and alternative hypotheses of urbanisation and 2/ explore their ability to simulate observed patterns in a virtual laboratory. The paper is therefore divided into two sections : an overview of the mechanisms implemented to represent competing hypotheses used to simulate urban evolution; and an evaluation of the resulting model structures in their ability to simulate - efficiently and parsimoniously - a system of cities (the Former Soviet Union) over several periods of time (before and after the crash of the USSR). We do so using a modular framework of model-building and evolutionary algorithms for the calibration of several model structures. This project aims at tackling equifinality in systems dynamics by confronting different mechanisms with similar evaluation criteria. It enables the identification of the best-performing models with respect to the chosen criteria by scanning automatically the parameter along with the space of model structures (as combinations of modelled dynamics).Comment: 21 pages, 3 figures, working pape

OpenMOLE, a workflow engine specifically tailored for the distributed exploration of simulation models

International audienceComplex-systems describe multiple levels of collective structure and organization. In such systems, the emergence of global behaviour from local interactions is generally studied through large scale experiments on numerical models. This analysis generates important computation loads which require the use of multi-core servers, clusters or grid computing. Dealing with such large scale executions is especially challenging for modellers who don't possess the theoretical and methodological skills required to take advantage of high performance computing environments. That's why we have designed a cloud approach for model experimentation. This approach has been implemented in OpenMOLE (Open MOdel Experiment) as a Domain Specific Language (DSL) that leverages the naturally parallel aspect of model experiments. The OpenMOLE DSL has been designed to explore user-supplied models. It delegates transparently their numerous executions to remote execution environment. From a user perspective, those environments are viewed as services providing computing power, therefore no technical detail is ever exposed. This paper presents the OpenMOLE DSL through the example of a toy model exploration and through the automated calibration of a real-world complex system model in the field of geography

Simulation numérique d'un écoulement de convection mixte en aval d'une marche

International audienceDes expériences numériques d'un écoulement en aval d'une marche horizontale sont menées en régime laminaire instationnaire, afin d'étudier les mécanismes thermodynamiques générés par la convection mixte dans un canal bidimensionnel de géométrie fixe. On se propose de quantifier l'influence de divers écarts de température dT entre la température de la base du canal et la température du fluide entrant. En analysant l'évolution temporelle de la température, on constate que l'écoulement est stationnaire lorsque dT est inférieur à 4 K, puis est périodique aux alentours de 5 K. Au-delà de cette valeur, le signal temporel de la température montre un comportement apériodique

The Importance of Being Hybrid for Spatial Epidemic Models: A Multi-Scale Approach

International audienceThis work addresses the spread of a disease within an urban system, defined as a network of interconnected cities. The first step consists of comparing two different approaches: a macroscopic one, based on a system of coupled Ordinary Differential Equations (ODE) Susceptible-Infected-Recovered (SIR) systems exploiting populations on nodes and flows on edges (so-called metapopulational model), and a hybrid one, coupling ODE SIR systems on nodes and agents traveling on edges. Under homogeneous conditions (mean field approximation), this comparison leads to similar results on the outputs on which we focus (the maximum intensity of the epidemic, its duration and the time of the epidemic peak). However, when it comes to setting up epidemic control strategies, results rapidly diverge between the two approaches, and it appears that the full macroscopic model is not completely adapted to these questions. In this paper, we focus on some control strategies, which are quarantine, avoidance and risk culture, to explore the differences, advantages and disadvantages of the two models and discuss the importance of being hybrid when modeling and simulating epidemic spread at the level of a whole urban system

Quantitative Structure-Permeation Relationships for Solute Transport Across Silicone Membranes

Purpose. The purpose of this work was to assess the molecular properties that influence solute permeation across silicone membranes and to compare the results with transport across human skin. Methods. The permeability coefficients (log K p) of a series of model solutes across silicone membranes were determined from the analysis of simple transport experiments using a pseudosteady-state mathematical model of the diffusion process. Subsequently, structure-permeation relationships were constructed and examined, focusing in particular on the difference between solute octanol/water and 1,2-dichloroethane/water partition coefficients (Δlog P oct-dce), which reported upon H-bond donor activity, and the computationally derived molecular hydrogen-bonding potential. Results. The hydrogen-bond donor acidity and the lipophilicity of the compounds examined greatly influenced their permeation across silicone membranes. Furthermore, for a limited dataset, a significant correlation was identified between solute permeation across silicone membranes and that through human epidermis. Conclusion. The key molecular properties that control solute permeation across silicone membranes have been identified. For the set of substituted phenols and other unrelated compounds examined here, a similar structure-permeation relationship has been derived for their transport through human epidermis, suggesting application of the results to the prediction of flux across biological barrier

H1N1 2009 Pandemic Influenza Virus: Resistance of the I223R Neuraminidase Mutant Explained by Kinetic and Structural Analysis

Two classes of antiviral drugs, neuraminidase inhibitors and adamantanes, are approved for prophylaxis and therapy against influenza virus infections. A major concern is that antiviral resistant viruses emerge and spread in the human population. The 2009 pandemic H1N1 virus is already resistant to adamantanes. Recently, a novel neuraminidase inhibitor resistance mutation I223R was identified in the neuraminidase of this subtype. To understand the resistance mechanism of this mutation, the enzymatic properties of the I223R mutant, together with the most frequently observed resistance mutation, H275Y, and the double mutant I223R/H275Y were compared. Relative to wild type, KMvalues for MUNANA increased only 2-fold for the single I223R mutant and up to 8-fold for the double mutant. Oseltamivir inhibition constants (KI) increased 48-fold in the single I223R mutant and 7500-fold in the double mutant. In both cases the change was largely accounted for by an increased dissociation rate constant for oseltamivir, but the inhibition constants for zanamivir were less increased. We have used X-ray crystallography to better understand the effect of mutation I223R on drug binding. We find that there is shrinkage of a hydrophobic pocket in the active site as a result of the I223R change. Furthermore, R223 interacts with S247 which changes the rotamer it adopts and, consequently, binding of the pentoxyl substituent of oseltamivir is not as favorable as in the wild type. However, the polar glycerol substituent present in zanamivir, which mimics the natural substrate, is accommodate

: Recueil de fiches pédagogiques du réseau MAPS

DoctoralLe réseau thématique MAPS «Modélisation multi-Agent appliquée aux Phénomènes Spatialisés » propose depuis 2009 des évènements scientifiques ayant pour but de diffuser les pratiques de modélisations multi-agents au sein des Sciences de l’Homme et de la Société (SHS). Ce collectif pluridisciplinaire de chercheurs, d’enseignants-chercheurs et de doctorants est labellisé en tant que ≪ réseau thématique » par le Réseau National des Systèmes Complexes (GIS RNSC) et bénéficie du soutien du CNRS au titre de la Formation Permanente. Depuis 2009, plusieurs modèles ont été développés au cours d'événements MAPS. Ces modèles ont fait l'objet de fiches pédagogiques détaillées destinées aux communautés éducatives et universitaires et en particulier aux enseignants qui souhaiteraient faire découvrir la modélisation à leurs étudiants, mais aussi à ceux qui envisagent d’approfondir certains aspects avec un public plus averti. Elles sont également destinées à tous les curieux qui souhaiteraient découvrir ce que la modélisation apporte aux SHS, du point de vue heuristique et du point de vue opérationnel. Enfin, elles sont aussi des supports pour toutes les personnes qui souhaiteraient diffuser les réflexions scientifiques sur la modélisation et la simulation qui ont présidé à la rédaction de ces fiches

Risk factors for mortality among children under 5 years of age with severe diarrhea in low- and middle-income countries: Findings from the WHO-coordinated Global Rotavirus and Pediatric Diarrhea Surveillance Networks.

BACKGROUND: Diarrhea is the second leading cause of death in children under five years of age globally. The burden of diarrheal mortality is concentrated in low-resource settings. Little is known about the risk factors for childhood death from diarrheal disease in low and middle-income countries. METHODS: Data from the WHO-coordinated Global Rotavirus and Pediatric Diarrhea Surveillance Networks, which are composed of active, sentinel, hospital-based surveillance sites, were analyzed to assess mortality in children less than five years of age who were hospitalized with diarrhea between 2008-2018. Case fatality risks were calculated, and multivariable logistic regression was performed to identify risk factors for mortality. RESULTS: This analysis is comprised of 234,781 cases, including 1,219 deaths, across 57 countries. The overall case fatality risk was found to be 0.5%. Risk factors for death in the multivariable analysis included younger age (for <6 months compared with older ages, OR = 3.54; 95% CI = 2.81-4.50), female sex (OR = 1.18; 95% CI= 1.06-1.81), presenting with persistent diarrhea (OR = 1.91; 95% CI= 1.01-3.25), no vomiting (OR = 1.13, 95% CI= 0.98-1.30), severe dehydration (OR = 3.79; 95% CI = 3.01-4.83), and being negative for rotavirus on an ELISA test (OR = 2.29; 95% CI= 1.92-2.74). Cases from the African Region had the highest odds of death compared with other WHO Regions (OR = 130.62 comparing the African Region to the European region; 95% CI= 55.72-422.73), while cases from the European region had the lowest odds of death. CONCLUSIONS: Our findings support known risk factors for childhood diarrheal mortality and highlight the need for interventions to address dehydration and rotavirus-negative diarrheal infections

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe