scholarly journals Individual-Based Modelling of Bacterial Ecologies and Evolution

2004 ◽  
Vol 5 (1) ◽  
pp. 100-104 ◽  
Author(s):  
C. Vlachos ◽  
R. Gregory ◽  
R. C. Paton ◽  
J. R. Saunders ◽  
Q. H. Wu
Keyword(s):  
Computational Models ◽  
Classifier Systems ◽  
Simulation Experiments ◽  
Computational Tools ◽  
Agent Based ◽  
Fine Grained ◽  
Learning Classifier ◽  
Potential Applications ◽  
The Individual ◽  
Behavioural Strategies

This paper presents two approaches to the individual-based modelling of bacterial ecologies and evolution using computational tools. The first approach is a fine-grained model that is based on networks of interactivity between computational objects representing genes and proteins. The second approach is a coarser-grained, agent-based model, which is designed to explore the evolvability of adaptive behavioural strategies in artificial bacteria represented by learning classifier systems. The structure and implementation of these computational models is discussed, and some results from simulation experiments are presented. Finally, the potential applications of the proposed models to the solution of real-world computational problems, and their use in improving our understanding of the mechanisms of evolution, are briefly outlined.

Computing Bacterial Evolvability Using Individual-Based Models

2011 ◽  
pp. 190-223
Author(s):  
Richard Gergory ◽  
Richard Vlachos ◽  
Ray C. Paton ◽  
John W. Palmer ◽  
Q. H. Wu ◽  
...  
Keyword(s):  
Parallel Machines ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Bacterial Cells ◽  
Classifier Systems ◽  
Bacterial Evolution ◽  
Fine Grained ◽  
Learning Classifier ◽  
History Of ◽  
The Individual

This chapter describes two approaches to individual-based modelling that are based on bacterial evolution and bacterial ecologies. Some history of the individual-based modelling approach is presented and contrasted to traditional methods. Two related models of bacterial evolution are then discussed in some detail. The first model consists of populations of bacterial cells, each bacterial cell containing a genome and many gene products derived from the genome. The genomes themselves are slowly mutated over time. As a result, this model contains multiple time scales and is very fine-grained. The second model employs a coarser-grained, agent-based architecture designed to explore the evolvability of adaptive behavioural strategies in artificial bacterial ecologies. The organisms in this approach are represented by mutating learning classifier systems. Finally, the subject of computability on parallel machines and clusters is applied to these models, with the aim of making them efficiently scalable to the point of being biologically realistic by containing sufficient numbers of complex individuals.

scholarly journals Agent-Based and System Dynamics Modeling of Water Field Services

Challenges ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 13
Author(s):  
Bernard Amadei
Keyword(s):  
System Dynamics ◽  
Computational Models ◽  
Software Modeling ◽  
Dynamics Modeling ◽  
Water Utility ◽  
Modeling Tools ◽  
Agent Based ◽  
Water Field ◽  
The Individual

This paper explores the applicability of the agent-based (AB) and system dynamics (SD) methods to model a case study of the management of water field services. Water borehole sites are distributed over an area and serve the water needs of a population. The equipment at all borehole sites is managed by a single water utility that has adopted specific repair, replacement, and maintenance rules and policies. The water utility employs several service crews initially stationed at a single central location. The crews respond to specific operation and maintenance requests. Two software modeling tools (AnyLogic and STELLA) are used to explore the benefits and limitations of the AB and SD methods to simulate the dynamic being considered. The strength of the AB method resides in its ability to capture in a disaggregated way the mobility of the individual service crews and the performance of the equipment (working, repaired, replaced, or maintained) at each borehole site. The SD method cannot capture the service crew dynamics explicitly and can only model the average state of the equipment at the borehole sites. Their differences aside, both methods offer policymakers the opportunity to make strategic, tactical, and logistical decisions supported by integrated computational models.

Towards a New Approach to Cross-Cultural Distinctiveness and Typicality of Human Faces: The Cross-Group Typicality/Distinctiveness Metric

2018 ◽  
Author(s):  
Karel Kleisner ◽  
Šimon Pokorný ◽  
Selahattin Adil Saribay
Keyword(s):  
Cross Cultural ◽  
Facial Appearance ◽  
Alternative Measure ◽  
Home Culture ◽  
Facial Stimuli ◽  
The Two Cultures ◽  
Potential Applications ◽  
The Individual ◽  
Human Faces ◽  
Ingroup Outgroup

In present research, we took advantage of geometric morphometrics to propose a data-driven method for estimating the individual degree of facial typicality/distinctiveness for cross-cultural (and other cross-group) comparisons. Looking like a stranger in one’s home culture may be somewhat stressful. The same facial appearance, however, might become advantageous within an outgroup population. To address this fit between facial appearance and cultural setting, we propose a simple measure of distinctiveness/typicality based on position of an individual along the axis connecting the facial averages of two populations under comparison. The more distant a face is from its ingroup population mean towards the outgroup mean the more distinct it is (vis-à-vis the ingroup) and the more it resembles the outgroup standards. We compared this new measure with an alternative measure based on distance from outgroup mean. The new measure showed stronger association with rated facial distinctiveness than distance from outgroup mean. Subsequently, we manipulated facial stimuli to reflect different levels of ingroup-outgroup distinctiveness and tested them in one of the target cultures. Perceivers were able to successfully distinguish outgroup from ingroup faces in a two-alternative forced-choice task. There was also some evidence that this task was harder when the two faces were closer along the axis connecting the facial averages from the two cultures. Future directions and potential applications of our proposed approach are discussed.

scholarly journals A novel geo-hierarchical population mobility model for spatial spreading of resurgent epidemics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexandru Topîrceanu ◽  
Radu-Emil Precup
Keyword(s):  
Computational Models ◽  
Human Mobility ◽  
Discrete Event ◽  
Mobility Model ◽  
Hierarchical Organization ◽  
Population Mobility ◽  
Mobility Patterns ◽  
Fine Grained ◽  
Epidemic Size ◽  
Control Of Epidemics

AbstractComputational models for large, resurgent epidemics are recognized as a crucial tool for predicting the spread of infectious diseases. It is widely agreed, that such models can be augmented with realistic multiscale population models and by incorporating human mobility patterns. Nevertheless, a large proportion of recent studies, aimed at better understanding global epidemics, like influenza, measles, H1N1, SARS, and COVID-19, underestimate the role of heterogeneous mixing in populations, characterized by strong social structures and geography. Motivated by the reduced tractability of studies employing homogeneous mixing, which make conclusions hard to deduce, we propose a new, very fine-grained model incorporating the spatial distribution of population into geographical settlements, with a hierarchical organization down to the level of households (inside which we assume homogeneous mixing). In addition, population is organized heterogeneously outside households, and we model the movement of individuals using travel distance and frequency parameters for inter- and intra-settlement movement. Discrete event simulation, employing an adapted SIR model with relapse, reproduces important qualitative characteristics of real epidemics, like high variation in size and temporal heterogeneity (e.g., waves), that are challenging to reproduce and to quantify with existing measures. Our results pinpoint an important aspect, that epidemic size is more sensitive to the increase in distance of travel, rather that the frequency of travel. Finally, we discuss implications for the control of epidemics by integrating human mobility restrictions, as well as progressive vaccination of individuals.

A Model for Cooperative Design Based on Multi-Agent System

2011 ◽  
Vol 65 ◽  
pp. 160-164
Author(s):  
Na Li ◽  
Yi Guo
Keyword(s):  
Information Exchange ◽  
Multi Agent System ◽  
Design Environment ◽  
Knowledge Processing ◽  
Cooperative Design ◽  
Agent Based ◽  
Fine Grained ◽  
Design Activities ◽  
Multi Agent ◽  
Design Result

Distributed cooperative design is carried out by teams located at different places. The regional limitation must be overcome to facilitate information exchange, knowledge processing, and design result exchange, etc., among the teams. This paper proposes a multi-agent based model for cooperative design. The model consists of five types of agents according to cooperative design environment and design activities. Integrated fine grained security mechanism into different agents is the major feature of this model.

scholarly journals Agent Based Models of Polymicrobial Biofilms and the Microbiome—A Review

2021 ◽  
Vol 9 (2) ◽  
pp. 417
Author(s):  
Sherli Koshy-Chenthittayil ◽  
Linda Archambault ◽  
Dhananjai Senthilkumar ◽  
Reinhard Laubenbacher ◽  
Pedro Mendes ◽  
...  
Keyword(s):  
Species Interactions ◽  
Computational Models ◽  
Human Microbiome ◽  
Agent Based Modeling ◽  
Autonomous Agent ◽  
Agent Based Models ◽  
Agent Based ◽  
Complex Interactions ◽  
Mucosal Surfaces ◽  
Living Organisms

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.

Rule Fitness and Pathology in Learning Classifier Systems

2004 ◽  
Vol 12 (1) ◽  
pp. 99-135 ◽  
Author(s):  
Tim Kovacs
Keyword(s):  
Value Functions ◽  
Classifier Systems ◽  
Reward Function ◽  
Learning Classifier ◽  
Learning Tasks ◽  
General Rules ◽  
Strength Based ◽  
The Difference ◽  
The Value Function ◽  
Do So

It has long been known that in some relatively simple reinforcement learning tasks traditional strength-based classifier systems will adapt poorly and show poor generalisation. In contrast, the more recent accuracy-based XCS, appears both to adapt and generalise well. In this work, we attribute the difference to what we call strong over general and fit over general rules. We begin by developing a taxonomy of rule types and considering the conditions under which they may occur. In order to do so an extreme simplification of the classifier system is made, which forces us toward qualitative rather than quantitative analysis. We begin with the basics, considering definitions for correct and incorrect actions, and then correct, incorrect, and overgeneral rules for both strength and accuracy-based fitness. The concept of strong overgeneral rules, which we claim are the Achilles' heel of strength-based classifier systems, are then analysed. It is shown that strong overgenerals depend on what we call biases in the reward function (or, in sequential tasks, the value function). We distinguish between strong and fit overgeneral rules, and show that although strong overgenerals are fit in a strength-based system called SB-XCS, they are not in XCS. Next we show how to design fit overgeneral rules for XCS (but not SB-XCS), by introducing biases in the variance of the reward function, and thus that each system has its own weakness. Finally, we give some consideration to the prevalence of reward and variance function bias, and note that non-trivial sequential tasks have highly biased value functions.

FG-LiquID

2021 ◽  
Vol 5 (3) ◽  
pp. 1-27
Author(s):  
Yumeng Liang ◽  
Anfu Zhou ◽  
Huanhuan Zhang ◽  
Xinzhe Wen ◽  
Huadong Ma
Keyword(s):  
Alcohol Concentration ◽  
Coarse Grained ◽  
Robust Identification ◽  
Fine Grained ◽  
Fresh Milk ◽  
Average Accuracy ◽  
Coarse Grained Material ◽  
Potential Applications ◽  
Fine Grained Material ◽  
The Difference

Contact-less liquid identification via wireless sensing has diverse potential applications in our daily life, such as identifying alcohol content in liquids, distinguishing spoiled and fresh milk, and even detecting water contamination. Recent works have verified the feasibility of utilizing mmWave radar to perform coarse-grained material identification, e.g., discriminating liquid and carpet. However, they do not fully exploit the sensing limits of mmWave in terms of fine-grained material classification. In this paper, we propose FG-LiquID, an accurate and robust system for fine-grained liquid identification. To achieve the desired fine granularity, FG-LiquID first focuses on the small but informative region of the mmWave spectrum, so as to extract the most discriminative features of liquids. Then we design a novel neural network, which uncovers and leverages the hidden signal patterns across multiple antennas on mmWave sensors. In this way, FG-LiquID learns to calibrate signals and finally eliminate the adverse effect of location interference caused by minor displacement/rotation of the liquid container, which ensures robust identification towards daily usage scenarios. Extensive experimental results using a custom-build prototype demonstrate that FG-LiquID can accurately distinguish 30 different liquids with an average accuracy of 97%, under 5 different scenarios. More importantly, it can discriminate quite similar liquids, such as liquors with the difference of only 1% alcohol concentration by volume.

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