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63 results for “agent-based”
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 Modelling for Archaeologists - Walk Through Guide
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Data from: Analysis and prediction of effects of the Manchester Triage System on patient waiting times in an emergency department by means of agent-based simulation
A simulation of complex clinical processes is a challenging task and suitable methods need to be found which can capture the influence of relevant factors and their relationships. The Manchester triage system (MTS) is widely used in German emergency departments (ED), however the impact on patient waiting times remain difficult to predict. The purpose of this work is the assessment of MTS particularly with regard to the waiting times of different degrees of severity. The methodology of agent based simulation was found suitable for the ED domain and the agent based simulation tool SeSAm was chosen due to its intuitive user interface and easy adaption of the simulation models. Altogether four agent classes could be implemented based on the information derived from a process model. The model permits a dynamic simulation of the ED processes and a reliable assessment of patient waiting times. In addition, the implementation of a triage nurse allowed the simulation of the triage process and a direct comparison to the current state without a standardized triage procedure. Essential influencing factors (e.g. number of patients, manning level) were implemented and their effects on the ED processes and patient waiting times assessed. The simulation runs delivered correct results based on the underlying process model and the collected statistical data. The process flow and the waiting times of an ED could be mapped exactly. In all simulation runs the waiting times of high triage levels (MTS-levels 1 and 2) could be reduced. Especially patients of MTS-level 2 in the waiting area of the ED benefit significantly from the implementation of a standardized triage procedure and the associated permanent monitoring.
Data from: Towards a framework for agent-based image analysis of remote-sensing data
Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects' properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).
Vosaroxin for Intermediate 2 or High-risk MDS After Failure With Hypomethylating Agent-based Therapy
ClinicalTrials.gov study NCT01980056. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: How new concepts become universal scientific approaches – insights from citation network analysis of agent-based complex systems science
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Data from: Analysis and prediction of effects of the Manchester Triage System on patient waiting times in an emergency department by means of agent-based simulation
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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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Data from: Towards a framework for agent-based image analysis of remote-sensing data
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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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ScienceDex guides
Understand access before you commit
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.