Innovative in silico approaches to address avian flu using grid technology

The recent years have seen the emergence of diseases which have spread very quickly all around the world either through human travels like SARS or animal migration like avian flu. Among the biggest challenges raised by infectious emerging diseases, one is related to the constant mutation of the viruses which turns them into continuously moving targets for drug and vaccine discovery. Another challenge is related to the early detection and surveillance of the diseases as new cases can appear just anywhere due to the globalization of exchanges and the circulation of people and animals around the earth, as recently demonstrated by the avian flu epidemics. For 3 years now, a collaboration of teams in Europe and Asia has been exploring some innovative in silico approaches to better tackle avian flu taking advantage of the very large computing resources available on international grid infrastructures. Grids were used to study the impact of mutations on the effectiveness of existing drugs against H5N1 and to find potentially new leads active on mutated strains. Grids allow also the integration of distributed data in a completely secured way. The paper presents how we are currently exploring how to integrate the existing data sources towards a global surveillance network for molecular epidemiology.Comment: 7 pages, submitted to Infectious Disorders - Drug Target

Behaviour Analysis of Interdependent Critical Infrastructure Components upon Failure

Urban life increasingly depends on intact critical infrastructures (CIs). For this reason, protecting critical infrastructure systems from natural disasters and man-made hazards has become an important topic in urban development research in recent years as a prerequisite for building and optimizing smart cities. To increase efficiency, the connections between CIs have been strengthened increasingly, resulting in highly interdependent large-scale infrastructure systems that are vulnerable to cascading failures. Hence, studying the cascading and feedback effects caused by the failure of a CI component in a given system can help strengthen this system. Understanding the response of the system in the event of a disaster can lead to better disaster management and better planning of critical infrastructures in the future. The population heavily depends on water, electricity, and the transportation network. These three components also depend on each other to function individually. This complex nature of interdependencies must be studied in order to understand the effects induced in one system due to the failure of another. The three systems (water, transport, and electricity) and their interdependencies can be modeled using graph theory. Water, transport, and electricity networks can be further broken down into smaller components. For example, the water network comprises water treatment plants, water storage tanks, pumping stations, sewage treatment, etc. interdependency factors into the model when, for instance, a pumping station depends on electricity. Graph theory can be used to depict the pairwise relationship between the individual components. Each node in the graph represents a critical infrastructure and the edges between these critical infrastructures represent their dependency. The modeled graph is a multigraph (inter-network dependency) and multidirectional (mutual dependence of two or more components). The idea behind building this model is to simulate the response of the interdependent systems upon failure. Building a simulation tool with an underlying interdependency graph model can not only help in understanding the failure response, but can also help in building a robust system for preserving the infrastructures. The data obtained from the simulation results will contribute to a better emergency response in the event of a disaster. The failure response of a system depends largely on the failed component. Hence, three cases are considered to simulate and identify the state of the system upon failure of a component: The failed component can be a node with maximum outward dependencies, a node with maximum inward dependencies, or a random failure of a component. If a component has the maximum number of outward edges, the simulation tool will help visualize the cascading effects, whereas a system with the maximum number of incoming edges will contribute to the understanding of the feedback response as the outward nodes are not affected immediately. Another goal of CI failure analysis is to develop an algorithm for the partial restoration of specific critical services when a CI is not working at full capacity. The selection of critical infrastructure components for restoration is based on the number of people being affected

Applying control theories and ABM to improve resilience-based design of systems

Applying optimal control theories and agent base modeling to improve resilience assessment of systems is a new field which has not been explored yet. A resilience decision support system should include some critical elements: (i) Assess risk, (ii) identify choices (Identify choices for reducing vulnerability that focus on joint solutions across social, economic, and ecological systems; provide decision support, including Web-based guidance and scenarios to assess options) and (iii) take actions (Help communities develop and implement solutions). The field of structural control provides loops which are able to approach the problem in a more rational way and provide practical solutions to the resilience design strategies. The paper describes the concept and provides some promising applications of the proposed interdisciplinary approach

Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...

There are indications that the current generation of simulation models in practical, operational uses has reached the limits of its usefulness under existing specifications. The relative stasis in operational urban modeling contrasts with simulation efforts in other disciplines, where techniques, theories, and ideas drawn from computation and complexity studies are revitalizing the ways in which we conceptualize, understand, and model real-world phenomena. Many of these concepts and methodologies are applicable to operational urban systems simulation. Indeed, in many cases, ideas from computation and complexity studies—often clustered under the collective term of geocomputation, as they apply to geography—are ideally suited to the simulation of urban dynamics. However, there exist several obstructions to their successful use in operational urban geographic simulation, particularly as regards the capacity of these methodologies to handle top-down dynamics in urban systems. This paper presents a framework for developing a hybrid model for urban geographic simulation and discusses some of the imposing barriers against innovation in this field. The framework infuses approaches derived from geocomputation and complexity with standard techniques that have been tried and tested in operational land-use and transport simulation. Macro-scale dynamics that operate from the topdown are handled by traditional land-use and transport models, while micro-scale dynamics that work from the bottom-up are delegated to agent-based models and cellular automata. The two methodologies are fused in a modular fashion using a system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of residential location has been developed with a view to hybridization. The model mixes cellular automata and multi-agent approaches and is formulated so as to interface with meso-models at a higher scale

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Complexity and the Economics of Climate Change: a Survey and a Look Forward

URL des Documents de travail : http://ces.univ-paris1.fr/cesdp/cesdp2016.htmlDocuments de travail du Centre d'Economie de la Sorbonne 2016.58 - ISSN : 1955-611XWe provide a survey of the micro and macro economics of climate change from a complexity science perspective and we discuss the challenges ahead for this line of research. We identify four areas of the literature where complex system models have already produced valuable insights: (i) coalition formation and climate negotiations, (ii) macroeconomic impacts of climate-related events, (iii) energy markets and (iv) diffusion of climate-friendly technologies. On each of these issues, accounting for heterogeneity, interactions and disequilibrium dynamics provides a complementary and novel perspective to the one of standard equilibrium models. Furthermore, it highlights the potential economic benefits of mitigation and adaptation policies and the risk of under-estimating systemic climate change-related risks

Piezoelectric vibration energy harvesting from airflow in HVAC (Heating Ventilation and Air Conditioning) systems

This study focuses on the design and wind tunnel testing of a high efficiency Energy Harvesting device, based on piezoelectric materials, with possible applications for the sustainability of smart buildings, structures and infrastructures. The development of the device was supported by ESA (the European Space Agency) under a program for the space technology transfer in the period 2014-2016. The EH device harvests the airflow inside Heating, Ventilation and Air Conditioning (HVAC) systems, using a piezoelectric component and an appropriate customizable aerodynamic appendix or fin that takes advantage of specific airflow phenomena (vortex shedding and galloping), and can be implemented for optimizing the energy consumption inside buildings. Focus is given on several relevant aspects of wind tunnel testing: different configurations for the piezoelectric bender (rectangular, cylindrical and T-shaped) are tested and compared, and the effective energy harvesting potential of a working prototype device is assessed

Eras of electric vehicles: electric mobility on the Verge. Focus Attention Scale

Daily or casual passenger vehicles in cities have negative burden on our finite world. Transport sector has been one of the main contributors to air pollution and energy depletion. Providing alternative means of transport is a promising strategy perceived by motor manufacturers and researchers. The paper presents the battery electric vehicles-BEVs bibliography that starts with the early eras of invention up till 2015 outlook. It gives a broad overview of BEV market and its technology in a chronological classification while sheds light on the stakeholders’ focus attentions in each stage, the so called, Focus-Attention-Scale-FAS. The attention given in each era is projected and parsed in a scale graph, which varies between micro, meso, and macro-scale. BEV-system is on the verge of experiencing massive growth; however, the system entails a variety of substantial challenges. Observations show the main issues of BEVsystem that require more attention followed by the authors’ recommendations towards an emerging market

An Agent Operationalization Approach for Context Specific Agent-Based Modeling

The potential of agent-based modeling (ABM) has been demonstrated in various research fields. However, three major concerns limit the full exploitation of ABM; (i) agents are too simple and behave unrealistically without any empirical basis, (ii) \'proof of concept\' applications are too theoretical and (iii) too much value placed on operational validity instead of conceptual validity. This paper presents an operationalization approach to determine the key system agents, their interaction, decision-making and behavior for context specific ABM, thus addressing the above-mentioned shortcomings. The approach is embedded in the framework of Giddens\' structuration theory and the structural agent analysis (SAA). The agents\' individual decision-making (i.e. reflected decisions) is operationalized by adapting the analytical hierarchy process (AHP). The approach is supported by empirical system knowledge, allowing us to test empirically the presumed decision-making and behavioral assumptions. The output is an array of sample agents with realistic (i.e. empirically quantified) decision-making and behavior. Results from a Swiss mineral construction material case study illustrate the information which can be derived by applying the proposed approach and demonstrate its practicability for context specific agent-based model development.Agent Operationalization, Decision-Making, Analytical Hierarchy Process (AHP), Agent-Based Modeling, Conceptual Validation