#575 Dealing with Complexity in Life Cycle Sustainability Assessment: The Case for US Transportation System
Authors: Nuri Onat and Murat Kucukvar
Quantitative life cycle sustainable assessment requires a complex and multi-dimensional understanding, which cannot be fully covered by the current portfolio of reductionist-oriented tools. Therefore, there is a dire need on a new generation of modeling tools and approaches that can quantitatively assess the economic, social and environmental dimensions of sustainability in an integrated way. To this end, this research aims to present a practical and novel approach for : (1) understanding the dynamic complexity of transportation sustainability for the triple-bottom-line impacts of alternative vehicles, and finally; (2) investigating the impacts of various vehicle-specific scenarios as a novel approach for selection of a macro-level functional unit considering all of the complex interactions in the environmental, social, and economic aspects; (3) broadening the existing Life Cycle Sustainability Assessment (LCSA) framework by considering macro-level environmental, economic and social impacts (termed as the triple-bottom-line), simultaneously; (4) deepening the existing LCSA framework by capturing the complex dynamic relationships between social, environmental, and economic indicators through causal loop modeling.
#584 Network analysis for the arboviruses traffic in coastal ecosystems: host-vector system and emergence
Authors: Richard Hoyos, Yesid Madrid, Nelson Fernandez and Juan Carlos Gallego-Gómez
Coastal ecosystems are natural foci of mosquitoes, arboviruses and vertebrate reservoirs, which interactions display complex relationships that explain the emergence or “Jump” to alternative hosts, such as human populations. However, the studies about interactions networks involved in arboviral transmission are limited, mainly because the methodology used for the relationship establishment are based on the frequencies calculation, scarcely reflexing the ecological relationship between vertebrate-host and mosquitoes. Also, it is no clear how transmission interactions in natural areas could change when ecosystems are impacted by human intervention and the disequilibrium of the natural patterns take place.
In this context, this study focuses on the characterization and analysis of the interaction networks of arboviruses, mangrove fauna, and human beings. Hence, we detected arboviruses belonging to two genus (Alphavirus/Flavivirus) between 2011 – 2014, in wild mosquitoes from a different ecosystems associated with a coastal zone in Colombia. Molecular techniques and sequencing were made for detecting virus and blood-meal identification. The information characterized about mosquitoes, virus detected and blood-meal identification was used for estimate interaction networks. Networks were created from connectivity maps of epidemiological agents and their interactions. Un-directed, symmetrical, and weighted networks were generated for each kind of biotope and biocoenosis. A summing network was obtained to analyze the whole ecosystem. Networks visualization was performed using Cytoscape and Igraph R package. The results indicate that some mosquito species are the vector-bridge with his extensive capacity of feeding for several host-species. Also, the high representation for avian fauna on inferring networks and possible ways for viral traffic suggest the possible emergence of some pathogens as West Nile virus, St. Louis encephalitis virus, Yellow Fever virus, and Venezuelan Equine Encephalitis virus. It was clear that the computational approach of network analysis enriches our base-knowledge about infectious diseases and their transmission.
#585 A Framework for Analysis of Attacker-Defender Interaction in Cyber Systems
Author: Alexander Outkin
Cyber systems is one of the most complex systems created by man. A key issue in understanding cyber systems is how to create simple, analytically tractable, and yet practically insightful models. We build upon an existing model called FlipIt and our own work extending it to a probabilistic attacker and defender playing for control over a resource. We present an incomplete information game-theoretic model of the attacker-defender interaction. Using the martingale-based approach, we solve analytically for defender strategies over time. We compare the analytical solution to a simulation and present the simulation results for cases that can not be treated analytically. We further compare and contrast with existing approaches based on Stackelberg equilibria.
#586 Studying crowd conditions from Wi-Fi positioning data in the Amsterdam Arena
Authors: Philip Rutten, Sonja Georgievska, Jan Amoraal, Alexey Dudko, Michael Lees, Spiros Koulouzis and Sander Klous
In this poster we present the Amsterdam Arena project, which involves observing and managing crowd behaviour, using the Amsterdam Arena stadium as a living laboratory. The main scientific question we explore is how to detect anomalous behaviour in large crowds in real time. The main purpose is to be able to predict a possible crowd disaster and identify means to prevent it from happening.
Human crowds are complex systems, and predicting or controlling their behaviour is challenging. Our approach involves three phases, firstly data collection from Wi-Fi and Bluetooth sensors in the stadium, secondly data analytics, and finally we aim to use simulation to make forecasts about the crowd dynamics.
In this poster I present initial results on the data analytics and show how we can extract density maps from Wi-Fi positioning data. The technology we deploy is based on the Wi-Fi signals from smart phones. We use the existing network of Wi-Fi access points in the stadium, and capture probe signals from smart phones, which are processed and anonymised in alignment with privacy concerns. The positions of smart phones are reconstructed using received signal strengths and methods similar to trilateration. The data provides us with real-time information on spatial distributions of crowd density, which is an essential indicator of the criticality of crowd conditions. To visualise the spatiotemporal behaviour of crowd density in real-time we dynamically generate heat maps along a moving time interval. The heat maps are generated through the statistical modelling of the positioning data. The generation of dynamic heat maps allows us to detect and locate hot-spots of density where crowd conditions reach critical values that could possibly lead to a disaster.
Authors: Nuri Onat and Murat Kucukvar
Quantitative life cycle sustainable assessment requires a complex and multi-dimensional understanding, which cannot be fully covered by the current portfolio of reductionist-oriented tools. Therefore, there is a dire need on a new generation of modeling tools and approaches that can quantitatively assess the economic, social and environmental dimensions of sustainability in an integrated way. To this end, this research aims to present a practical and novel approach for : (1) understanding the dynamic complexity of transportation sustainability for the triple-bottom-line impacts of alternative vehicles, and finally; (2) investigating the impacts of various vehicle-specific scenarios as a novel approach for selection of a macro-level functional unit considering all of the complex interactions in the environmental, social, and economic aspects; (3) broadening the existing Life Cycle Sustainability Assessment (LCSA) framework by considering macro-level environmental, economic and social impacts (termed as the triple-bottom-line), simultaneously; (4) deepening the existing LCSA framework by capturing the complex dynamic relationships between social, environmental, and economic indicators through causal loop modeling.
#584 Network analysis for the arboviruses traffic in coastal ecosystems: host-vector system and emergence
Authors: Richard Hoyos, Yesid Madrid, Nelson Fernandez and Juan Carlos Gallego-Gómez
Coastal ecosystems are natural foci of mosquitoes, arboviruses and vertebrate reservoirs, which interactions display complex relationships that explain the emergence or “Jump” to alternative hosts, such as human populations. However, the studies about interactions networks involved in arboviral transmission are limited, mainly because the methodology used for the relationship establishment are based on the frequencies calculation, scarcely reflexing the ecological relationship between vertebrate-host and mosquitoes. Also, it is no clear how transmission interactions in natural areas could change when ecosystems are impacted by human intervention and the disequilibrium of the natural patterns take place.
In this context, this study focuses on the characterization and analysis of the interaction networks of arboviruses, mangrove fauna, and human beings. Hence, we detected arboviruses belonging to two genus (Alphavirus/Flavivirus) between 2011 – 2014, in wild mosquitoes from a different ecosystems associated with a coastal zone in Colombia. Molecular techniques and sequencing were made for detecting virus and blood-meal identification. The information characterized about mosquitoes, virus detected and blood-meal identification was used for estimate interaction networks. Networks were created from connectivity maps of epidemiological agents and their interactions. Un-directed, symmetrical, and weighted networks were generated for each kind of biotope and biocoenosis. A summing network was obtained to analyze the whole ecosystem. Networks visualization was performed using Cytoscape and Igraph R package. The results indicate that some mosquito species are the vector-bridge with his extensive capacity of feeding for several host-species. Also, the high representation for avian fauna on inferring networks and possible ways for viral traffic suggest the possible emergence of some pathogens as West Nile virus, St. Louis encephalitis virus, Yellow Fever virus, and Venezuelan Equine Encephalitis virus. It was clear that the computational approach of network analysis enriches our base-knowledge about infectious diseases and their transmission.
#585 A Framework for Analysis of Attacker-Defender Interaction in Cyber Systems
Author: Alexander Outkin
Cyber systems is one of the most complex systems created by man. A key issue in understanding cyber systems is how to create simple, analytically tractable, and yet practically insightful models. We build upon an existing model called FlipIt and our own work extending it to a probabilistic attacker and defender playing for control over a resource. We present an incomplete information game-theoretic model of the attacker-defender interaction. Using the martingale-based approach, we solve analytically for defender strategies over time. We compare the analytical solution to a simulation and present the simulation results for cases that can not be treated analytically. We further compare and contrast with existing approaches based on Stackelberg equilibria.
#586 Studying crowd conditions from Wi-Fi positioning data in the Amsterdam Arena
Authors: Philip Rutten, Sonja Georgievska, Jan Amoraal, Alexey Dudko, Michael Lees, Spiros Koulouzis and Sander Klous
In this poster we present the Amsterdam Arena project, which involves observing and managing crowd behaviour, using the Amsterdam Arena stadium as a living laboratory. The main scientific question we explore is how to detect anomalous behaviour in large crowds in real time. The main purpose is to be able to predict a possible crowd disaster and identify means to prevent it from happening.
Human crowds are complex systems, and predicting or controlling their behaviour is challenging. Our approach involves three phases, firstly data collection from Wi-Fi and Bluetooth sensors in the stadium, secondly data analytics, and finally we aim to use simulation to make forecasts about the crowd dynamics.
In this poster I present initial results on the data analytics and show how we can extract density maps from Wi-Fi positioning data. The technology we deploy is based on the Wi-Fi signals from smart phones. We use the existing network of Wi-Fi access points in the stadium, and capture probe signals from smart phones, which are processed and anonymised in alignment with privacy concerns. The positions of smart phones are reconstructed using received signal strengths and methods similar to trilateration. The data provides us with real-time information on spatial distributions of crowd density, which is an essential indicator of the criticality of crowd conditions. To visualise the spatiotemporal behaviour of crowd density in real-time we dynamically generate heat maps along a moving time interval. The heat maps are generated through the statistical modelling of the positioning data. The generation of dynamic heat maps allows us to detect and locate hot-spots of density where crowd conditions reach critical values that could possibly lead to a disaster.
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