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Agent-based modelling (ABM) has its roots in the modeling of systems where complex behaviors arise from simple rules (interactions).
Built around four conceptual groups of market agents (user, manufacturer, infrastructure provider, authorities) it is designed to capture the key decision rules, interactions and feedbacks within the system between 1995 and 2050.
Through set of rules, interactions, and simulated mechanism of asymmetrical heredity, a phenomenological description of the evolution of the agents' characteristics was defined and maintained throughout the simulations.
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Therefore, policy makers concerned with gas and electricity market designs should take into account the results of network rules interaction.
This ordering accounts for rule interactions, respects specificity considerations and facilitates the construction of coherent states of beliefs.
This is unlike the long germ embryo of Drosophila, whose segmentation utilizes pair-rule interactions only during the late blastoderm stage.
These results suggest that p62 ZZ domain is a structural and functional homolog of the UBR box of N-recognins and that Nt-Arg interacts with p62 ZZ domain through the N-end rule interaction.
In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction.
These results collectively suggest that the N-end rule interaction of Nt-Arg with ZZ domain induces a conformational change, exposing PB1 domain and LC3 interaction region for polymerization and interaction with LC3, respectively.
However, the predicted mood × stop rule interaction was not found, and there was no influence of gender, pain severity, and pain catastrophizing on task performance.
We take a holistic approach that accounts for procedural rules, actors' interactions, institutions and values, and individuals' characteristics.
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CEO of Professional Science Editing for Scientists @ prosciediting.com