Distributed simulation of city inundation by coupled surface and subsurface porous flow for urban flood decision support system

We present a decision support system for flood early warning and disaster management. It includes the models for data-driven meteorological predictions, for simulation of atmospheric pressure, wind, long sea waves and seiches; a module for optimization of flood barrier gates operation; models for stability assessment of levees and embankments, for simulation of city inundation dynamics and citizens evacuation scenarios. The novelty of this paper is a coupled distributed simulation of surface and subsurface flows that can predict inundation of low-lying inland zones far from the submerged waterfront areas, as observed in St. Petersburg city during the floods. All the models are wrapped as software services in the CLAVIRE platform for urgent computing, which provides workflow management and resource orchestration.Comment: Pre-print submitted to the 2013 International Conference on Computational Scienc

Inference of the Russian drug community from one of the largest social networks in the Russian Federation

The criminal nature of narcotics complicates the direct assessment of a drug community, while having a good understanding of the type of people drawn or currently using drugs is vital for finding effective intervening strategies. Especially for the Russian Federation this is of immediate concern given the dramatic increase it has seen in drug abuse since the fall of the Soviet Union in the early nineties. Using unique data from the Russian social network 'LiveJournal' with over 39 million registered users worldwide, we were able for the first time to identify the on-line drug community by context sensitive text mining of the users' blogs using a dictionary of known drug-related official and 'slang' terminology. By comparing the interests of the users that most actively spread information on narcotics over the network with the interests of the individuals outside the on-line drug community, we found that the 'average' drug user in the Russian Federation is generally mostly interested in topics such as Russian rock, non-traditional medicine, UFOs, Buddhism, yoga and the occult. We identify three distinct scale-free sub-networks of users which can be uniquely classified as being either 'infectious', 'susceptible' or 'immune'.Comment: 12 pages, 11 figure

VIRTUAL REALITY FOR MANAGEMENT OF SITUATIONAL AWARENESS DURING GLOBAL MASS GATHERINGS

This paper presents a training technology for staff of mass events for development of action skills in large gatherings of people, including crowd dynamic management and actions in extreme situations caused by the panic. The technology is based on the multi-agent model of crowd dynamic with dynamically re-computable navigation fields. We implemented the software system that provides a collaborative and distributed process of training activities in the virtual reality environment. The following characteristics of the developed software system available from experimental studies were analyzed: computational intensity of simulations, scalability of rendering system and reactivity of the final system when rendering computationally intensive scenes. The proposed models and infrastructure for training through collaborative immersion in the virtual reality can improve situational awareness of events staff prior to the event. The developed technology is a unique tool for improving the quality and safety of disposable and unique events involving the broad masses of people, including unfunded by retrospective experience mass gatherings. Developed technology was tested within the Kumbh Mela festival in Ujjain, India

Young Russian researchers take up challenges in the computational sciences

Abstract is not available in fulltext