Dynamical and bursty interactions in social networks

We present a modeling framework for dynamical and bursty contact networks made of agents in social interaction. We consider agents' behavior at short time scales, in which the contact network is formed by disconnected cliques of different sizes. At each time a random agent can make a transition from being isolated to being part of a group, or vice-versa. Different distributions of contact times and inter-contact times between individuals are obtained by considering transition probabilities with memory effects, i.e. the transition probabilities for each agent depend both on its state (isolated or interacting) and on the time elapsed since the last change of state. The model lends itself to analytical and numerical investigations. The modeling framework can be easily extended, and paves the way for systematic investigations of dynamical processes occurring on rapidly evolving dynamical networks, such as the propagation of an information, or spreading of diseases

Mathematical modeling of tumor therapy with oncolytic viruses: Effects of parametric heterogeneity on cell dynamics

One of the mechanisms that ensure cancer robustness is tumor heterogeneity, and its effects on tumor cells dynamics have to be taken into account when studying cancer progression. There is no unifying theoretical framework in mathematical modeling of carcinogenesis that would account for parametric heterogeneity. Here we formulate a modeling approach that naturally takes stock of inherent cancer cell heterogeneity and illustrate it with a model of interaction between a tumor and an oncolytic virus. We show that several phenomena that are absent in homogeneous models, such as cancer recurrence, tumor dormancy, an others, appear in heterogeneous setting. We also demonstrate that, within the applied modeling framework, to overcome the adverse effect of tumor cell heterogeneity on cancer progression, a heterogeneous population of an oncolytic virus must be used. Heterogeneity in parameters of the model, such as tumor cell susceptibility to virus infection and virus replication rate, can lead to complex, time-dependent behaviors of the tumor. Thus, irregular, quasi-chaotic behavior of the tumor-virus system can be caused not only by random perturbations but also by the heterogeneity of the tumor and the virus. The modeling approach described here reveals the importance of tumor cell and virus heterogeneity for the outcome of cancer therapy. It should be straightforward to apply these techniques to mathematical modeling of other types of anticancer therapy.Comment: 45 pages, 6 figures; submitted to Biology Direc

Ontology-based collaborative framework for disaster recovery scenarios

This paper aims at designing of adaptive framework for supporting collaborative work of different actors in public safety and disaster recovery missions. In such scenarios, firemen and robots interact to each other to reach a common goal; firemen team is equipped with smart devices and robots team is supplied with communication technologies, and should carry on specific tasks. Here, reliable connection is mandatory to ensure the interaction between actors. But wireless access network and communication resources are vulnerable in the event of a sudden unexpected change in the environment. Also, the continuous change in the mission requirements such as inclusion/exclusion of new actor, changing the actor's priority and the limitations of smart devices need to be monitored. To perform dynamically in such case, the presented framework is based on a generic multi-level modeling approach that ensures adaptation handled by semantic modeling. Automated self-configuration is driven by rule-based reconfiguration policies through ontology

An extension to iStar framework as alternative to support design decisions in the task analysis performed in the Human Computer Interaction Area (HCI)

Abstract The goal of this work is to present i* framework as alternative to support design decisions in the Human Computer Interaction Area (HCI), mainly, in the process of task analysis. We show i* framework a modeling language suitable for an early phase of software system modeling to understand the problem domain, also, that provides methods to represent tasks, as well as support design decisions the development that satisfies the process requirements from the beginning until the end. To achieve our goal, approaches addressed to analyze notations aimed at task analysis and their representation. Finally, we present suggestions for improvement in the i* framework through a proposed extension for refining task model, instantiating examples and justifying our proposal through peer review.Abstract The goal of this work is to present i* framework as alternative to support design decisions in the Human Computer Interaction Area (HCI), mainly, in the process of task analysis. We show i* framework a modeling language suitable for an early phase of software system modeling to understand the problem domain, also, that provides methods to represent tasks, as well as support design decisions the development that satisfies the process requirements from the beginning until the end. To achieve our goal, approaches addressed to analyze notations aimed at task analysis and their representation. Finally, we present suggestions for improvement in the i* framework through a proposed extension for refining task model, instantiating examples and justifying our proposal through peer review