![]() Furthermore, in accordance with experimental evidence our model showed that shorter oxic/anoxic periods exhibit a faster increase of total Fe ³⁺ reduction rate than longer periods. Efficient iron-nanoparticle reduction is confined to pH around 6, being twice as high than at pH 7, whereas at pH 5 negligible reduction takes place. We predicted that the beneficial effect of a high number of iron-nanoparticles on the total Fe ³⁺ reduction rate of the system is not only due to the faster reduction of these iron-nanoparticles, but also to the nanoparticles’ additional capacity to bind Fe ²⁺ on their surfaces. We compared (i) combinations of different Fe ³⁺ -reducing/Fe ²⁺ -oxidizing modes of action of the bacteria and (ii) system behaviour for different pH values. By including the key processes of reduction/oxidation, movement, adhesion, Fe ²⁺ -equilibration and nanoparticle formation, we derive a core model which enables hypothesis testing and prediction for different environmental conditions including temporal cycles of oxic and anoxic conditions. and the microaerophilic ferrous iron (Fe ²⁺ )-oxidizing bacteria Sideroxydans spp. In this paper, we present the first computational agent-based model of microbial iron cycling, between the anaerobic ferric iron (Fe ³⁺ )-reducing bacteria Shewanella spp. Such bacteria often co-occur at oxic-anoxic gradients in aquatic and terrestrial habitats. Ifelse (patch-value-right > 0.Iron-reducing and iron-oxidizing bacteria are of interest in a variety of environmental and industrial applications. If (d > max-agents-in-panic-emotional) Īsk patches Set waiting-for waiting-for + 1 set moving-for 0 set state "wait" Ifelse not any? other turtles-on patch-ahead speed If (prob >= r) ] do with prob 0.75Įnd to-report get-direction-to-dest If (rational? and in-panic-for = r) ] do with prob 0.75 If (rational? and in-panic-for > 0 and has-changed?) [ set prob (1 / (9 + (0.644 ^ -0))) no change CHANGE with prob 0 If (rational? and in-panic-for 0 and not has-changed?) [ Set prob (1 / (9 + (0.644 ^ -0))) no change CHANGE WITH with prob 0.1 set prob (1 / (9 + (0.644 ^ -0))) no change CHANGE WITH with prob 0 set prob int (prob * 10) if (prob = r) Set curr-dir get-direction-to-dest curr-exit sets up persons, based on 1: number of persons 2: rational persons % creates environment with 2 exits (left and right), environment type: can be 1, 2 or 3 1 is symmetric, 2 is asymmetric, and 3 is (one exit) invisible, and floor field ![]() Updated? initial-target-exit dom spread-update-left? spread-update-right? helping environment varaibles Patches-own [structure-type walkable? exit-id doms steps-to-exits patch-value-left patch-value-right variables representing environment Speed using-left using-right last-left last-right max-agents-in-panic-rational max-agents-in-panic-emotional max-panic-value Tleft tright total number of persons exitting from left and right To appear: "An Agent-Based Model of Crowd Evacuation: Combining Individual, Social and Technological Aspects" in ACM SIGSIM PADS 2020 scheduled on June 2020 ( ).īreed agents that need to exit NETLOGO FEATURES RELATED MODELS CREDITS AND REFERENCES Use different environment types, difffernt population densities and % of rational agents and three exit behavior strategies. GO: Let the persons exit using one of the two exits, left or right, based on one of the three strategies used. SETUP: Sets up the environment, and generates two types of agents (persons). ![]() ![]() Our model explores relationship of panic (or not panic) with decision-making while agents are exiting from one of the two exits. Based on the simulation results, a couple of useful recommendations are also given. By simulating these models, an insight into the effectiveness of several interesting evacuation scenarios is provided. In this paper, we addressed this challenge by combining individual, social and technological models of people during evacuation, while pivoting all these aspects on a common agent-based modeling framework and a grid-based hypothetical environment. However, incorporation of different aspect categories in a unified modeling space is a challenge. Evacuation modeling and simulation is used to analyze various possible outcomes as different scenarios unfold, typically when the complexity of scenario is high. Do you have questions or comments about this model?ĭevelopment of crowd evacuation systems is a challenge due to involvement of complex interrelated aspects, diversity of involved individuals and/or environment, and lack of direct evidence.
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