Generating self-organizing collective behavior using separation dynamics from experimental data

Mathematical models for systems of interacting agents using simple local rules have been proposed and shown to exhibit emergent swarming behavior. Most of these models are constructed by intuition or manual observations of real phenomena, and later tuned or verified to simulate desired dynamics. In contrast to this approach, we propose using a model that attempts to follow an averaged rule of the essential distance-dependent collective behavior of real pigeon flocks, which was abstracted from experimental data. By using a simple model to follow the behavioral tendencies of real data, we show that our model can exhibit emergent self-organizing dynamics such as flocking, pattern formation, and counter-rotating vortices. The range of behaviors observed in our simulations are richer than the standard models of collective dynamics, and should thereby give potential for new models of complex behavior.Comment: Submitted to Chao

Reciprocal relationships in collective flights of homing pigeons

Collective motion of bird flocks can be explained via the hypothesis of many wrongs, and/or, a structured leadership mechanism. In pigeons, previous studies have shown that there is a well-defined hierarchical structure and certain specific individuals occupy more dominant positions --- suggesting that leadership by the few individuals drives the behavior of the collective. Conversely, by analyzing the same data-sets, we uncover a more egalitarian mechanism. We show that both reciprocal relationships and a stratified hierarchical leadership are important and necessary in the collective movements of pigeon flocks. Rather than birds adopting either exclusive averaging or leadership strategies, our experimental results show that it is an integrated combination of both compromise and leadership which drives the group's movement decisions.Comment: 7 pages, 5 figure

Dynamical modeling of collective behavior from pigeon flight data: flock cohesion and dispersion

Several models of flocking have been promoted based on simulations with qualitatively naturalistic behavior. In this paper we provide the first direct application of computational modeling methods to infer flocking behavior from experimental field data. We show that this approach is able to infer general rules for interaction, or lack of interaction, among members of a flock or, more generally, any community. Using experimental field measurements of homing pigeons in flight we demonstrate the existence of a basic distance dependent attraction/repulsion relationship and show that this rule is sufficient to explain collective behavior observed in nature. Positional data of individuals over time are used as input data to a computational algorithm capable of building complex nonlinear functions that can represent the system behavior. Topological nearest neighbor interactions are considered to characterize the components within this model. The efficacy of this method is demonstrated with simulated noisy data generated from the classical (two dimensional) Vicsek model. When applied to experimental data from homing pigeon flights we show that the more complex three dimensional models are capable of predicting and simulating trajectories, as well as exhibiting realistic collective dynamics. The simulations of the reconstructed models are used to extract properties of the collective behavior in pigeons, and how it is affected by changing the initial conditions of the system. Our results demonstrate that this approach may be applied to construct models capable of simulating trajectories and collective dynamics using experimental field measurements of herd movement. From these models, the behavior of the individual agents (animals) may be inferred

Most Plastic Products Release Estrogenic Chemicals: A Potential Health Problem That Can Be Solved

Background: Chemicals having estrogenic activity (EA) reportedly cause many adverse health effects, especially at low (picomolar to nanomolar) doses in fetal and juvenile mammals

An overview of chemical additives present in plastics: Migration, release, fate and environmental impact during their use, disposal and recycling

Over the last 60 years plastics production has increased manifold, owing to their inexpensive, multipurpose, durable and lightweight nature. These characteristics have raised the demand for plastic materials that will continue to grow over the coming years. However, with increased plastic materials production, comes increased plastic material wastage creating a number of challenges, as well as opportunities to the waste management industry. The present overview highlights the waste management and pollution challenges, emphasising on the various chemical substances (known as “additives”) contained in all plastic products for enhancing polymer properties and prolonging their life. Despite how useful these additives are in the functionality of polymer products, their potential to contaminate soil, air, water and food is widely documented in literature and described herein. These additives can potentially migrate and undesirably lead to human exposure via e.g. food contact materials, such as packaging. They can, also, be released from plastics during the various recycling and recovery processes and from the products produced from recyclates. Thus, sound recycling has to be performed in such a way as to ensure that emission of substances of high concern and contamination of recycled products is avoided, ensuring environmental and human health protection, at all times

Structural identification of GMA models: Algorithm and model comparison

We propose a local search algorithm for structural identification of Generalized Mass Action (GMA) models from time course data. The algorithm has been implemented as part of our existing system for identification of dynamical systems. We compare this approach to existing alternatives in terms of analytical GMA models, analytical GMA models with parameter estimation from time course data, S-systems, and linear models. This is done on three new test problems designed to exhibit different characteristic properties of biochemical pathways, and which are defined with chemical rate reactions. By applying state-of-the-art algorithmic methods we are able to make a full investigation for the test problems also with noisy data. The results show that on the tested problems, our structural identification algorithm is able to find as good or better models than any of the other approaches. It can therefore be expected to be a useful tool for identifying models of unknown systems from time course data. All test problems are available on the web. Copyright 2010 ACM

Structural identification of GMA models: Algorithm and model comparison

We propose a local search algorithm for structural identification of Generalized Mass Action (GMA) models from time course data. The algorithm has been implemented as part of our existing system for identification of dynamical systems. We compare this approach to existing alternatives in terms of analytical GMA models, analytical GMA models with parameter estimation from time course data, S-systems, and linear models. This is done on three new test problems designed to exhibit different characteristic properties of biochemical pathways, and which are defined with chemical rate reactions. By applying state-of-the-art algorithmic methods we are able to make a full investigation for the test problems also with noisy data. The results show that on the tested problems, our structural identification algorithm is able to find as good or better models than any of the other approaches. It can therefore be expected to be a useful tool for identifying models of unknown systems from time course data. All test problems are available on the web. Copyright 2010 ACM

Simulations of high density instances () of the R2 model.

<p>Low and high initial speeds are considered. The simulation with high initial speed shows small groups dispersing in many directions. Plot (a) shows a snapshot after 100 s and (b) one after 500 s.</p