The Virtual Reality applied to the biology understanding: the in virtuo experimentation

International audienceThe advent of the computer and computer science, and in particular virtual reality, offers new experiment possibilities with numerical simulations and introduces a new type of investigation for the complex systems study: the in virtuo experiment. This work lies on the framework of multi-agent systems. We propose a generic model for systems biology based on reification of the interactions, on a concept of organization and on a multi-model approach. By 'reification' we understand that interactions are considered as autonomous agents. The aim has been to combine the systemic paradigm and the virtual reality to provide an application able to collect, simulate, experiment and understand the knowledge owned by different biologists working around an interdisciplinary subject. Here, we have been focused on the urticaria disease understanding. Autonomy is taken as a principle. The method permits to integrate different natures of model in the same application using chaotic asynchronous iterations and C++ library: AReVi. We have modeled biochemical reactions, molecular 3D diffusion, cell organizations and mechanical 3D interactions. It also permits to embed different expert system modeling methods like fuzzy cognitive maps. This work provides a toolbox easily adaptable to new biological studies

Modeling Evacuation Risk Using a Stochastic Process Formulation of Mesoscopic Dynamic Network Loading

One of the actions usually conducted to limit exposure to a hazardous event is the evacuation of the area that is subject to the effects of the event itself. This involves modifications both to demand (a large number of users all want to move together) and to supply (the transport network may experience changes in capacity, unusable roads, etc.). In order to forecast the traffic evolution in a network during an evacuation, a natural choice is to adopt an approach based on Dynamic Traffic Assignment (DTA) models. However, such models typically give a deterministic prediction of future conditions, whereas evacuations are subject to considerable uncertainty. The aim of the present paper is to describe an evacuation approach for decision support during emergencies that directly predicts the time-evolution of the probability of evacuating users from an area, formulated within a discrete-time stochastic process modelling framework. The approach is applied to a small artificial case as well as a real-life network, where we estimate users' probabilities to reach a desired safe destination and analyze time dependent risk factors in an evacuation scenario

Crowd Disasters as Systemic Failures: Analysis of the Love Parade Disaster

Each year, crowd disasters happen in different areas of the world. How and why do such disasters happen? Are the fatalities caused by relentless behavior of people or a psychological state of panic that makes the crowd 'go mad'? Or are they a tragic consequence of a breakdown of coordination? These and other questions are addressed, based on a qualitative analysis of publicly available videos and materials, which document the planning and organization of the Love Parade in Duisburg, Germany, and the crowd disaster on July 24, 2010. Our analysis reveals a number of misunderstandings that have widely spread. We also provide a new perspective on concepts such as 'intentional pushing', 'mass panic', 'stampede', and 'crowd crushs'. The focus of our analysis is on the contributing causal factors and their mutual interdependencies, not on legal issues or the judgment of personal or institutional responsibilities. Video recordings show that, in Duisburg, people stumbled and piled up due to a 'domino effect', resulting from a phenomenon called 'crowd turbulence' or 'crowd quake'. Crowd quakes are a typical reason for crowd disasters, to be distinguished from crowd disasters resulting from 'panic stampedes' or 'crowd crushes'. In Duisburg, crowd turbulence was the consequence of amplifying feedback and cascading effects, which are typical for systemic instabilities. Accordingly, things can go terribly wrong in spite of no bad intentions from anyone. Comparing the incident in Duisburg with others, we give recommendations to help prevent future crowd disasters. In particular, we introduce a new scale to assess the criticality of conditions in the crowd. This may allow preventative measures to be taken earlier on. Furthermore, we discuss the merits and limitations of citizen science for public investigation, considering that today, almost every event is recorded and reflected in the World Wide Web.Comment: For a collection of links to complementary video materials see http://loveparadevideos.heroku.com/ For related work see http://www.soms.ethz.c

Dynamic traffic assignment-based modeling paradigms for sustainable transportation planning and urban development

textTransportation planning and urban development in the United States have synchronously emerged over the past few decades to encompass goals associated with sustainability, improved connectivity, complete streets and mitigation of environmental impacts. These goals have evolved in tandem with some of the relatively more traditional objectives of supply-side improvements such as infrastructure and capacity expansion. Apart from the numerous federal regulations in the US transportation sector that reassert sustainability motivations, metropolitan planning organizations and civic societies face similar concerns in their decision-making and policy implementation. However, overall transportation planning to incorporate these wide-ranging objectives requires characterization of large-scale transportation systems and traffic flow through them, which is dynamic in nature, computationally intense and a non-trivial problem. Thus, these contemporary questions lie at the interface of transportation planning, urban development and sustainability planning. They have the potential of being effectively addressed through state-of-the-art transportation modeling tools, which is the main motivation and philosophy of this thesis. From the research standpoint, some of these issues have been addressed in the past typically from the urban design, built-environment, public health and vehicle technology and mostly qualitative perspectives, but not as much from the traffic engineering and transportation systems perspective---a gap in literature which the thesis aims to fill. Specifically, it makes use of simulation-based dynamic traffic assignment (DTA) to develop modeling paradigms and integrated frameworks to seamlessly incorporate these in the transportation planning process. In addition to just incorporating them in the planning process, DTA-based paradigms are able to accommodate numerous spatial and temporal dynamics associated with system traffic, which more traditional static models are not able to. Besides, these features are critical in the context of the planning questions of this study. Specifically, systemic impacts of suburban and urban street pattern developments typically found in US cities in past decades of the 20th century have been investigated. While street connectivity and design evolution is mostly regulated through local codes and subdivision ordinances, its impacts on traffic and system congestion requires modeling and quantitative evidence which are explored in this thesis. On the environmental impact mitigation side, regional emission inventories from the traffic sector have also been quantified. Novel modeling approaches for the street connectivity-accessibility problem are proposed. An integrated framework using the Environmental Protection Agency's regulatory MOVES model has been developed, combining it with mesoscopic-level DTA simulation. Model demonstrations and applications on real and large-sized study areas reveal that different levels of connectivity and accessibility have substantial impacts on system-wide traffic---as connectivity levels reduce, traffic and congestion metrics show a gradually increasing trend. As regards emissions, incorporation of dynamic features leads to more realistic emissions inventory generation compared to default databases and modules, owing to consideration of the added dynamic features of system traffic and region-specific conditions. Inter-dependencies among these sustainability planning questions through the common linkage of traffic dynamics are also highlighted. In summary, the modeling frameworks, analyses and findings in the thesis contribute to some ongoing debates in planning studies and practice regarding ideal urban designs, provisions of sustainability and complete streets. Furthermore, the integrated emissions modeling framework, in addition to sustainability-related contributions, provides important tools to aid MPOs and state agencies in preparation of state implementation plans for demonstrating conformity to national ambient air-quality standards in their regions and counties. This is a critical condition for them to receive federal transportation funding.Civil, Architectural, and Environmental Engineerin

Kinetic Theory and Swarming Tools to Modeling Complex Systems—Symmetry problems in the Science of Living Systems

This MPDI book comprises a number of selected contributions to a Special Issue devoted to the modeling and simulation of living systems based on developments in kinetic mathematical tools. The focus is on a fascinating research field which cannot be tackled by the approach of the so-called hard sciences—specifically mathematics—without the invention of new methods in view of a new mathematical theory. The contents proposed by eight contributions witness the growing interest of scientists this field. The first contribution is an editorial paper which presents the motivations for studying the mathematics and physics of living systems within the framework an interdisciplinary approach, where mathematics and physics interact with specific fields of the class of systems object of modeling and simulations. The different contributions refer to economy, collective learning, cell motion, vehicular traffic, crowd dynamics, and social swarms. The key problem towards modeling consists in capturing the complexity features of living systems. All articles refer to large systems of interaction living entities and follow, towards modeling, a common rationale which consists firstly in representing the system by a probability distribution over the microscopic state of the said entities, secondly, in deriving a general mathematical structure deemed to provide the conceptual basis for the derivation of models and, finally, in implementing the said structure by models of interactions at the microscopic scale. Therefore, the modeling approach transfers the dynamics at the low scale to collective behaviors. Interactions are modeled by theoretical tools of stochastic game theory. Overall, the interested reader will find, in the contents, a forward look comprising various research perspectives and issues, followed by hints on to tackle these

On mapping epilepsy : magneto- and electroencephalographic characterizations of epileptic activities

Epilepsy is one of the most common neurological disorder, affecting up to 10 individuals per 1000 persons. The disorder have been known for several thousand years, with the first clinical descriptions dating back to ancient times. Nonetheless, characterization of the dynamics underlying epilepsy remains largely unknown. Understanding these patophysiological processes requires unifying both a neurobiological perspective, as well as a technically advanced neuroimaging perspective. The incomplete insight into epilepsy dynamics is reflected by the insufficient treatment options. Approximately 30% of all patients do not respond to anti-epileptic drugs (AEDs) and thus suffers from recurrent seizures despite adequate pharmacological treatments. These pharmacoresistant patients often undergo epilepsy surgery evaluations. Epilepsy surgery aims to resect the part of the brain that generates the epileptic seizure activity (seizure onset zone, SOZ). Nonetheless, up to 50% of all patients relapse after surgery. This can be due to incomplete mapping of both the SOZ and of other structures that might be involved in seizure initiation and propagation. Such cortical and subcortical structures are collectively referred to as the epileptic network. Historically, epilepsy was considered to be either a generalized disorder involving the entire brain, or a highly localized, focal, disorder. The modern technological development of both structural and functional neuroimaging has drastically altered this view. This development has made significant contributions to the now prevailing view that both generalized and focal epilepsies arise from more or less widespread pathological network pathways. Visualization of these pathways play an important role in the presurgical planning. Thus, both improved characterization and understanding of such pathways are pivotal in improvement of epilepsy diagnostics and treatments. It is evident that epilepsy research needs to stand on two legs: Both improved understanding of pathological, neurobiological and neurophysiological process, and improved neuroimaging instrumentation. Epilepsy research do not only span from visualization to understanding of neurophysiological processes, but also from cellular, neuronal, microscopic processes, to dynamical, large-scale network processes. It is well known that neurons involved in epileptic activities exhibit specific, pathological firing patterns. Genetic mutations resulting in neuronal ion channel defects can cause severe, and even lethal, epileptic syndromes in children, clearly illustrating a role for neuron membrane properties in epilepsy. However, cellular processes themselves cannot explain how epileptic seizures can involve, and propagate across, large cortical areas and generate seizure-specific symptomatologies. A strict cellular perspective can neither explain epilepsy-associated pathological interactions between larger distant regions in between seizures. Instead, the dynamical effects of cellular synchronization across both mesoscopic and macroscopic scales also need to be considered. Today, the only means to study such effects in human subjects are by combinations of neuroimaging modalities. However, as all measurement techniques, these exhibit individual limitations that affect the kind of information that can be inferred from these. Thus, once more we reach the conclusion that epilepsy research needs to rest upon both a neurophysiological/neurobiological leg, and a technical/instrumentational leg. In accordance with this necessity of a dual approach to epilepsy, this thesis covers both neurophysiological aspects of epileptic activity development, as well as functional neuroimaging instrumentation development with focus on epileptic activity detection and localization. Part 1 (neurophysiological part) is concerned with the neurophysiological dynamical changes that underlie development of so called interictal epileptiform discharges (IEDs) with special focus on the role of low-frequency oscillations. To this aim, both conventional magnetoencephalography (MEG) and intracranial electroencephalography (iEEG) with neurostimulation is analyzed. Part 2 (instrumentation part) is concerned with development of cutting-edge, novel on-scalp magnetoencephalography (osMEG) within clinical epilepsy evaluations and research with special focus on IEDs. The theses cover both modeling of osMEG characteristics, as well as the first-ever osMEG recording of a temporal lobe epilepsy patient