scholarly journals Effect of Nanodisks at Different Positions on the Fano Resonance of Graphene Heptamers

2019 ◽  
Vol 9 (20) ◽  
pp. 4345
Author(s):  
Zhou ◽  
Qiu ◽  
Wang ◽  
Ren ◽  
Zhao ◽  
...  
Keyword(s):  
Collective Behavior ◽  
Fano Resonance ◽  
Chemical Potential ◽  
Resonance Spectrum ◽  
Collective Behaviors ◽  
Biochemical Sensing ◽  
Graphene Nanodisks

The formation of Fano resonance based on graphene heptamers with D6h symmetry and the effect of nanoparticles at different positions on the collective behavior are investigated in this paper. The significances of central nanodisks on the whole structure are studied first by varying the chemical potential. In addition, the effect of six graphene nanodisks placed in the ring on collective behaviors is also investigated. The influence of the nanodisks at different positions of the ring on the Fano resonance spectrum of the whole oligomer is researched by changing the chemical potential and radius. The proposed nanostructures may find broad applications in the fields of chemical and biochemical sensing.

scholarly journals Unsupervised manifold learning of collective behavior

2020 ◽  
Author(s):  
Mathew Titus ◽  
George Hagstrom ◽  
James R. Watson
Keyword(s):  
Complex Systems ◽  
Prior Knowledge ◽  
Collective Behavior ◽  
Generative Models ◽  
Emergent Behavior ◽  
New Method ◽  
Collective Behaviors ◽  
State Classification ◽  
Macro Scale ◽  
Fish Schooling

AbstractCollective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative models, or other supervised learning techniques. Each of these cases requires knowledge of and a method for characterizing the macro-state(s) of the system. This presents a challenge for studying novel systems where there may be little prior knowledge. Here, we present a new unsupervised method of detecting emergent behavior in complex systems, and discerning between distinct collective behaviors. We require only metrics, d(1), d(2), defined on the set of agents, X, which measure agents’ nearness in variables of interest. We apply the method of diffusion maps to the systems (X, d(i)) to recover efficient embeddings of their interaction networks. Comparing these geometries, we formulate a measure of similarity between two networks, called the map alignment statistic (MAS). A large MAS is evidence that the two networks are codetermined in some fashion, indicating an emergent relationship between the metrics d(1) and d(2). Additionally, the form of the macro-scale organization is encoded in the covariances among the two sets of diffusion map components. Using these covariances we discern between different modes of collective behavior in a data-driven, unsupervised manner. This method is demonstrated on empirical fish schooling data. We show that our state classification subdivides the known behaviors of the school in a meaningful manner, leading to a finer description of the system’s behavior.Author summaryMany complex systems in society and nature exhibit collective behavior where individuals’ local interactions lead to system-wide organization. One challenge we face today is to identify and characterize these emergent behaviors, and here we have developed a new method for analyzing data from individuals, to detect when a given complex system is exhibiting system-wide organization. Importantly, our approach requires no prior knowledge of the fashion in which the collective behavior arises, or the macro-scale variables in which it manifests. We apply the new method to an agent-based model and empirical observations of fish schooling. While we have demonstrated the utility of our approach to biological systems, it can be applied widely to financial, medical, and technological systems for example.

scholarly journals Genetic architecture of collective behaviors in zebrafish

2018 ◽  
Author(s):  
Wenlong Tang ◽  
Guoqiang Zhang ◽  
Fabrizio Serluca ◽  
Jingyao Li ◽  
Xiaorui Xiong ◽  
...  
Keyword(s):  
Collective Behavior ◽  
Adrenergic Receptor ◽  
Genetic Architecture ◽  
Single Gene ◽  
Gene Mutations ◽  
Collective Actions ◽  
Adult Zebrafish ◽  
Inner Mitochondrial Membrane ◽  
Collective Behaviors ◽  
Species Survival

AbstractCollective behaviors of groups of animals, such as schooling and shoaling of fish, are central to species survival, but genes that regulate these activities are not known. Here we parsed collective behavior of groups of adult zebrafish using computer vision and unsupervised machine learning into a set of highly reproducible, unitary, several hundred millisecond states and transitions, which together can account for the entirety of relative positions and postures of groups of fish. Using CRISPR-Cas9 we then targeted for knockout 35 genes associated with autism and schizophrenia. We found mutations in three genes had distinctive effects on the amount of time spent in the specific states or transitions between states. Mutation in immp2l (inner mitochondrial membrane peptidase 2-like gene) enhances states of cohesion, so increases shoaling; mutation in in the Nav1.1 sodium channel, scn1lab+/− causes the fish to remain scattered without evident social interaction; and mutation in the adrenergic receptor, adra1aa−/−, keeps fish close together and retards transitions between states, leaving fish motionless for long periods. Motor and visual functions seemed relatively well-preserved. This work shows that the behaviors of fish engaged in collective activities are built from a set of stereotypical states. Single gene mutations can alter propensities to collective actions by changing the proportion of time spent in these states or the tendency to transition between states. This provides an approach to begin dissection of the molecular pathways used to generate and guide collective actions of groups of animals.

scholarly journals Bacterial bioconvection confers context-dependent growth benefits and is robust under varying metabolic and genetic conditions

2021 ◽  
Author(s):  
Daniel Shoup ◽  
Tristan Ursell
Keyword(s):  
Collective Behavior ◽  
Model Organism ◽  
Liquid Interface ◽  
Collective Behaviors ◽  
E Coli ◽  
Oxygen Gradients ◽  
Metabolic Conditions ◽  
Context Dependent ◽  
Dependent Growth ◽  
Air Liquid Interface

Microbial communities often respond to environmental cues by adopting collective behaviors--like biofilms or swarming--that benefit the population. Bioconvection is a distinct and robust collective behavior wherein microbes locally gather into dense groups and subsequently plume downward through fluid environments, driving flow and mixing on scales thousands of times larger than an individual cell. Though bioconvection was observed more than 100 years ago, effects of differing physical and chemical inputs, as well as its potential selective advantages to different species of microbes, remain largely unexplored. In the canonical microbial bioconvector Bacillus subtilis, density inversions that drive this flow are setup by vertically oriented oxygen gradients that originate from an air-liquid interface. In this work, we develop Escherichia coli as a complementary model organism for the study of bioconvection. We show that for E. coli and B. subtilis, bioconvection confers a context-dependent growth benefit with clear genetic correlates to motility and chemotaxis. We found that fluid depth, cell concentration, and carbon availability have complimentary effects on the emergence and timing of bioconvective patterns, and whereas oxygen gradients are required for B. subtilis bioconvection, we found that E. coli deficient in aerotaxis (Δaer) or energy-taxis (Δtsr) still bioconvect, as do cultures that lack an air-liquid interface. Thus, in two distantly related microbes, bioconvection confers context-dependent growth benefits, and E. coli bioconvection is robustly elicited by multiple types of chemotaxis. These results greatly expand the set of physical and metabolic conditions in which this striking collective behavior can be expected and demonstrate its potential to be a generic force for behavioral selection across ecological contexts.

scholarly journals Agent Based Modelling and Simulation of Pedestrian Crowds in Panic Situations

2020 ◽  
Vol 5 ◽  
pp. A100
Author(s):  
Mohammed Alrashed ◽  
Jeff Shamma
Keyword(s):  
Collective Behavior ◽  
Specific Form ◽  
Dynamics Model ◽  
Collective Behaviors ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Crowd Dynamics ◽  
Proposed Model ◽  
The Impact ◽  
Mass Events

The increasing occurrence of panic stampedes during mass events has motivated studying the impact of panic on crowd dynamics. Understanding the collective behaviors of panic stampedes is essential to reducing the risk of deadly crowd disasters. In this work, we use an agent-based formulation to model the collective human behavior in such crowd dynamics. We investigate the impact of panic behavior on crowd dynamics, as a specific form of collective behavior, by introducing a contagious panic parameter. The proposed model describes the intensity and spread of panic through the crowd. The corresponding panic parameter impacts each individual to represent a different variety of behaviors that can be associated with panic situations such as escaping danger, clustering, and pushing. Simulation results show contagious panic and pushing behavior, resulting in a more realistic crowd dynamics model.

Differentiation of Collective Behavior Based on Automated Discovery of Dynamical Kinds

2018 ◽  
Author(s):  
Amanda Hashimoto ◽  
Nicole Abaid ◽  
Subhradeep Roy ◽  
Benjamin Jantzen ◽  
Colin Shea-Blymyer
Keyword(s):  
Collective Behavior ◽  
Dynamical Symmetry ◽  
Order Parameters ◽  
Local Order ◽  
Global Order ◽  
Simulation Data ◽  
Collective Behaviors ◽  
Two Systems ◽  
Automated Discovery ◽  
Global And Local

In this paper, we explore a model of collective behavior using EUGENE, an algorithm for automated discovery of so-called “dynamical kinds”. Two systems are of the same dynamical kind if their underlying causal dynamics are similar, as defined using dynamical symmetry. We apply EUGENE to simulation data from a model capable of generating a range of qualitatively different collective behaviors, from aligned motion to circular milling. These behaviors are measured using both global and local order parameters, and this data is analyzed with EUGENE. We find that EUGENE is capable of differentiating between these systems when global order parameters are used, and can only identify more coarse characteristics when local order parameters are considered.

Modeling the FX Market Traders’ Behavior

2013 ◽  
pp. 303-338 ◽  
Author(s):  
Monira Aloud ◽  
Edward Tsang ◽  
Richard Olsen
Keyword(s):  
Collective Behavior ◽  
Agent Based Modeling ◽  
Statistical Properties ◽  
Trading Behavior ◽  
Stylized Facts ◽  
Collective Behaviors ◽  
Agent Based ◽  
Trading Agents ◽  
Individual Traders ◽  
Fx Market

In this chapter, the authors use an Agent-Based Modeling (ABM) approach to model trading behavior in the Foreign Exchange (FX) market. They establish statistical properties (stylized facts) of the traders’ trading behavior in the FX market using a high-frequency dataset of anonymised OANDA individual traders’ historical transactions on an account level spanning 2.25 years. Using the identified stylized facts of real FX market traders’ behavior, the authors evaluate the collective behavior of the trading agents in resembling the collective behavior of the FX market traders. The study identifies the conditions under which the stylized facts of trading agents’ collective behaviors resemble those for the real FX market traders’ collective behavior. The authors perform an exploration of the market’s features in order to identify the conditions under which the stylized facts emerge.

scholarly journals A Computer Model of Drafting Effects on Collective Behavior in Elite 10,000-m Runners

2017 ◽  
Vol 12 (3) ◽  
pp. 345-350 ◽  
Author(s):  
Hugh Trenchard ◽  
Andrew Renfree ◽  
Derek M. Peters
Keyword(s):  
Computer Simulations ◽  
Computer Model ◽  
Collective Behavior ◽  
Energy Savings ◽  
Performance Data ◽  
The Other ◽  
Psychological Processes ◽  
Collective Behaviors

Purpose:Drafting in cycling influences collective behavior of pelotons. Although evidence for collective behavior in competitive running events exists, it is not clear if this results from energetic savings conferred by drafting. This study modeled the effects of drafting on behavior in elite 10,000-m runners.Methods:Using performance data from a men’s elite 10,000-m track running event, computer simulations were constructed using Netlogo 5.1 to test the effects of 3 different drafting quantities on collective behavior: no drafting, drafting to 3 m behind with up to ~8% energy savings (a realistic running draft), and drafting up to 3 m behind with up to 38% energy savings (a realistic cycling draft). Three measures of collective behavior were analyzed in each condition: mean speed, mean group stretch (distance between first- and last-placed runner), and runner-convergence ratio (RCR), which represents the degree of drafting benefit obtained by the follower in a pair of coupled runners.Results:Mean speeds were 6.32 ± 0.28, 5.57 ± 0.18, and 5.51 ± 0.13 m/s in the cycling-draft, runner-draft, and no-draft conditions, respectively (all P < .001). RCR was lower in the cycling-draft condition but did not differ between the other 2. Mean stretch did not differ between conditions.Conclusions:Collective behaviors observed in running events cannot be fully explained through energetic savings conferred by realistic drafting benefits. They may therefore result from other, possibly psychological, processes. The benefits or otherwise of engaging in such behavior are as yet unclear.

scholarly journals Stress-induced collective behavior leads to the formation of multicellular structures and the survival of the unicellular alga Chlamydomonas

2021 ◽  
Author(s):  
Felix de Carpentier ◽  
Alexandre Maes ◽  
Christophe H Marchand ◽  
Celine Chung ◽  
Cyrielle Durand ◽  
...  
Keyword(s):  
Collective Behavior ◽  
Large Scale ◽  
Abiotic Stress Response ◽  
Collective Behaviors ◽  
Defense Systems ◽  
Toxic Environment ◽  
Response To Stress ◽  
Unicellular Organisms ◽  
Living Organisms ◽  
Multicellular Organisms

Depending on their nature, living organisms use various strategies to adapt to environmental stress conditions. Multicellular organisms implement a set of reactions involving signaling and cooperation between different types of cells. Unicellular organisms on the other hand must activate defense systems, which involve collective behaviors between individual organisms. In the unicellular model alga Chlamydomonas reinhardtii, the existence and the function of collective behavior mechanisms in response to stress remain largely unknown. Here we report the discovery of a mechanism of abiotic stress response that Chlamydomonas can trigger to form large multicellular structures that can comprise several thousand cells. We show that these aggregates constitute an effective bulwark within which the cells are efficiently protected from the toxic environment. We have generated the first family of mutants that aggregate spontaneously, the socializer mutants (saz), of which we describe here in detail saz1. We took advantage of the saz mutants to implement a large scale multiomics approach that allowed us to show that aggregation is not the result of passive agglutination, but rather genetic reprogramming and substantial modification of the secretome. The reverse genetic analysis we conducted on some of the most promising candidates allowed us to identify the first positive and negative regulators of aggregation and to make hypotheses on how this process is controlled in Chlamydomonas.

scholarly journals Continuous Multiagent Control Using Collective Behavior Entropy for Large-Scale Home Energy Management

2020 ◽  
Vol 34 (01) ◽  
pp. 922-929 ◽  
Author(s):  
Jianwen Sun ◽  
Yan Zheng ◽  
Jianye Hao ◽  
Zhaopeng Meng ◽  
Yang Liu
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Collective Behavior ◽  
Large Scale ◽  
Scale Up ◽  
Group Behavior ◽  
Power Cost ◽  
Collective Behaviors ◽  
Peak Loads ◽  
Home Energy Management

With the increasing popularity of electric vehicles, distributed energy generation and storage facilities in smart grid systems, an efficient Demand-Side Management (DSM) is urgent for energy savings and peak loads reduction. Traditional DSM works focusing on optimizing the energy activities for a single household can not scale up to large-scale home energy management problems. Multi-agent Deep Reinforcement Learning (MA-DRL) shows a potential way to solve the problem of scalability, where modern homes interact together to reduce energy consumers consumption while striking a balance between energy cost and peak loads reduction. However, it is difficult to solve such an environment with the non-stationarity, and existing MA-DRL approaches cannot effectively give incentives for expected group behavior. In this paper, we propose a collective MA-DRL algorithm with continuous action space to provide fine-grained control on a large scale microgrid. To mitigate the non-stationarity of the microgrid environment, a novel predictive model is proposed to measure the collective market behavior. Besides, a collective behavior entropy is introduced to reduce the high peak loads incurred by the collective behaviors of all householders in the smart grid. Empirical results show that our approach significantly outperforms the state-of-the-art methods regarding power cost reduction and daily peak loads optimization.

scholarly journals Informational architecture across non-living and living collectives

2021 ◽  
Author(s):  
Hyunju Kim ◽  
Gabriele Valentini ◽  
Jake Hanson ◽  
Sara Imari Walker
Keyword(s):  
Collective Behavior ◽  
Intelligent Systems ◽  
Degrees Of Freedom ◽  
Physical Models ◽  
Information Architecture ◽  
Biological Phenomenon ◽  
Future Directions ◽  
Collective Behaviors ◽  
Information Theoretic ◽  
Physical Systems

AbstractCollective behavior is widely regarded as a hallmark property of living and intelligent systems. Yet, many examples are known of simple physical systems that are not alive, which nonetheless display collective behavior too, prompting simple physical models to often be adopted to explain living collective behaviors. To understand collective behavior as it occurs in living examples, it is important to determine whether or not there exist fundamental differences in how non-living and living systems act collectively, as well as the limits of the intuition that can be built from simpler, physical examples in explaining biological phenomenon. Here, we propose a framework for comparing non-living and living collectives as a continuum based on their information architecture: that is, how information is stored and processed across different degrees of freedom. We review diverse examples of collective phenomena, characterized from an information-theoretic perspective, and offer views on future directions for quantifying living collective behaviors based on their informational structure.

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