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58 results for “agent based model”
Data from: The contribution of marine aggregate-associated bacteria to the accumulation of pathogenic bacteria in oysters: an agent-based model
Bivalves process large volumes of water, leading to their accumulation of bacteria, including potential human pathogens (e.g., vibrios). These bacteria are captured at low efficiencies when freely suspended in the water column, but they also attach to marine aggregates, which are captured with near 100% efficiency. For this reason, and because they are often enriched with heterotrophic bacteria, marine aggregates have been hypothesized to function as important transporters of bacteria into bivalves. The relative contribution of aggregates and unattached bacteria to the accumulation of these cells, however, is unknown. We developed an agent-based model to simulate accumulation of vibrio-type bacteria in oysters. Simulations were conducted over a realistic range of concentrations of bacteria and aggregates and incorporated the dependence of pseudofeces production on particulate matter. The model shows that the contribution of aggregate-attached bacteria depends strongly on the unattached bacteria, which form the colonization pool for aggregates and are directly captured by the simulated oysters. The concentration of aggregates is also important, but its effect depends on the concentration of unattached bacteria. At high bacterial concentrations, aggregates contribute the majority of bacteria in the oysters. At low concentrations of unattached bacteria, aggregates have a neutral or even a slightly negative effect on bacterial accumulation. These results provide the first evidence suggesting that the concentration of aggregates could influence uptake of pathogenic bacteria in bivalves and show that the tendency of a bacterial species to remain attached to aggregates is a key factor for understanding species-specific accumulation.
Data from: Can longitudinal generalized estimating equation models distinguish network influence and homophily? an agent-based modeling approach to measurement characteristics
Background: Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model. Methods: We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we varied the presence of homophily and network influence. We then used statistical regression and examined the GEE model performance in each cohort to determine whether the model was able to detect the presence of homophily and network influence. Results: In cohorts with both static and dynamic networks, we find that the GEE models have excellent sensitivity and reasonable specificity for determining the presence or absence of network influence, but little ability to distinguish whether or not homophily is present. Conclusions: The GEE models are a valuable tool to examine for the presence of network influence in longitudinal data, but are quite limited with respect to homophily.
Supplementary material 1 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
The code documentation for the ALMaSS Population_Manager class
Figure 2 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
Figure 2 The time step processes. The three parts of the time step (BeginStep, Step, EndStep) process can run in multithreaded mode for each object 1 to n, extant at that time and are separated by customisable methods for reporting or list management by the Population_Manager class.
Figure 1 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
Figure 1 The current class hierarchy for beetle population managers, starting with the parent class Population_Manager_Base.
Figure 3 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
Figure 3 Change in the maximum and minimum sizes and population numbers for two scenarios using the Theoretical1 species, N = do nothing, Rand = randomise the execution order.
COVID-19 PHAC Agent-Based Model for age-stratified intervention and herd immunity study
<p>The PHAC agent-based model was used to explore the progression of the COVID-19 epidemic under three intervention scenarios (infection-preventing vaccination, illness-preventing vaccination and shielding) in individuals above three age thresholds (≥45, ≥55 and ≥65 years) while lifting shutdowns and physical distancing in the community. This data repository contains the PHAC modelling code in Anylogic used for this study. </p>
Agent‐based modeling of the effects of forest dynamics, selective logging, and fragment size on epiphyte communities
<p>Forest canopies play a crucial role in structuring communities of vascular epiphytes by providing substrate for colonization, by locally varying microclimate, and by causing epiphyte mortality due to branch or tree fall. However, as field studies in the three-dimensional habitat of epiphytes are generally challenging, our understanding of how forest structure and dynamics influence the structure and dynamics of epiphyte communities is scarce. Mechanistic models can improve our understanding of epiphyte community dynamics. We present such a model that couples dispersal, growth, and mortality of individual epiphytes with substrate dynamics, obtained from a three-dimensional functional-structural forest model, allowing the study of forest-epiphyte interactions. After validating the epiphyte model with independent field data, we performed several theoretical simulation experiments to assess how (1) differences in natural forest dynamics, (2) selective logging, and (3) forest fragmentation could influence the long-term dynamics of epiphyte communities. The proportion of arboreal substrate occupied by epiphytes (i.e. saturation level) was tightly linked with forest dynamics and increased with decreasing forest turnover rates. While species richness was, in general, negatively correlated with forest turnover rates, low species numbers in forests with very low turnover rates were due to competitive exclusion when epiphyte communities became saturated. Logging had a negative impact on epiphyte communities, potentially leading to a near-complete extirpation of epiphytes when the simulated target diameters fell below a threshold. Fragment size had no effect on epiphyte abundance and saturation level but correlated positively with species numbers. Synthesis: The presented model is a first step towards studying the dynamic forest-epiphyte interactions in an agent-based modelling framework. Our study suggests forest dynamics as key factor in controlling epiphyte communities. Thus, both natural and human-induced changes in forest dynamics, e.g. increased mortality rates or the loss of large trees, pose challenges for epiphyte conservation.</p>
Agent-Based Modelling for Archaeologists - Walk Through Guide
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Data from: An agent-based model of anoikis in the colon crypt displays novel emergent behaviour consistent with biological observations
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Agent‐based modeling of the effects of forest dynamics, selective logging, and fragment size on epiphyte communities
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Data from: Modelling social care provision in an agent-based framework with kinship networks
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Data from: The contribution of marine aggregate-associated bacteria to the accumulation of pathogenic bacteria in oysters: an agent-based model
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Data from: Modeling the internet of things, self-organizing and other complex adaptive communication networks: a cognitive agent-based computing approach
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COVID-19 PHAC Agent-Based Model for age-stratified intervention and herd immunity study
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Data from: Can longitudinal generalized estimating equation models distinguish network influence and homophily? an agent-based modeling approach to measurement characteristics
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A comparison of model-based and model-free agents in solving semi-automatically generated PPDDL problems - Agent classification table
<p>This table classifies the agents used in the work to those which use models in their decision-making, which are model-based (MB), and those which do not, which are model-free (MF).</p>
Simulation results of an agent-based model of civil violence with the effect of introducing a small world network
<p>The data set contains the simulation results of an agent-based model of civil violence with the effect of introducing a small world network. Some details on the agent-based model (without small world network) can be found in [Maria Fonoberova, Vladimir A. Fonoberov, Igor Mezic, Jadranka Mezic and P. Jeffrey Brantingham, Nonlinear Dynamics of Crime and Violence in Urban Settings, Journal of Artificial Societies and Social Simulation, 15(1), 2, http://jasss.soc.surrey.ac.uk/15/1/2.html, DOI: 10.18564/jasss.1921].</p> <p>Files in folder "Appropriate_Rate_of_Violence" are related to the case with the appropriate rate of violence.</p> <p>Subfolder NoSWN_CitVis_14 is for the case with no small world network and the citizen vision of 14.</p> <p>Subfolder SWN_CitVis_13.16 is for the case with small world network and the citizen vision of 13.16.</p> <p>Subfolder SWN_CitVis_14 is for the case with small world network and the citizen vision of 14.</p> <p>Each file with name starting with Act has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. These files are provided for lattice sizes from 100x100 to 300x300 and for different random seeds.</p> <p>Files in folder "High_Rate_of_Violence" are related to the case with the high rate of violence.</p> <p>Subfolder Beta0.2 is for the case with small world network and \beta=0.2.</p> <p>Subfolder Beta0.8 is for the case with small world network and \beta=0.8.</p> <p>Subfolder NoSWN is for the case with no small world network.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. These files are provided for lattice sizes from 100x100 to 300x300 and for different random seeds.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.