scholarly journals The aging effect in evolving scientific citation networks

2021 ◽  
Vol 126 (5) ◽  
pp. 4297-4309
Author(s):  
Feng Hu ◽  
Lin Ma ◽  
Xiu-Xiu Zhan ◽  
Yinzuo Zhou ◽  
Chuang Liu ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Complex Dynamics ◽  
Simulation Analysis ◽  
Evolutionary Model ◽  
Aging Effect ◽  
Underlying Mechanism ◽  
Citation Networks ◽  
Citation Behavior ◽  
Quantitative Research Methods ◽  
Scientific Citation

AbstractThe study of citation networks is of interest to the scientific community. However, the underlying mechanism driving individual citation behavior remains imperfectly understood, despite the recent proliferation of quantitative research methods. Traditional network models normally use graph theory to consider articles as nodes and citations as pairwise relationships between them. In this paper, we propose an alternative evolutionary model based on hypergraph theory in which one hyperedge can have an arbitrary number of nodes, combined with an aging effect to reflect the temporal dynamics of scientific citation behavior. Both theoretical approximate solution and simulation analysis of the model are developed and validated using two benchmark datasets from different disciplines, i.e. publications of the American Physical Society (APS) and the Digital Bibliography & Library Project (DBLP). Further analysis indicates that the attraction of early publications will decay exponentially. Moreover, the experimental results show that the aging effect indeed has a significant influence on the description of collective citation patterns. Shedding light on the complex dynamics driving these mechanisms facilitates the understanding of the laws governing scientific evolution and the quantitative evaluation of scientific outputs.

scholarly journals Temporal dynamics of stomatal conductance of plants under water deficit: can homeostasis be improved by more complex dynamics?

2004 ◽  
Vol 47 (3) ◽  
pp. 423-431 ◽  
Author(s):  
Gustavo Maia Souza ◽  
Ricardo Ferraz de Oliveira ◽  
Victor José Mendes Cardoso
Keyword(s):  
Stomatal Conductance ◽  
Sugar Beet ◽  
Water Deficit ◽  
Time Series Data ◽  
Temporal Dynamics ◽  
Complex Dynamics ◽  
Fractal Dimensions ◽  
Series Data ◽  
Complex Behaviour ◽  
Lz Complexity

In this study we hypothesized that chaotic or complex behavior of stomatal conductance could improve plant homeostasis after water deficit. Stomatal conductance of sunflower and sugar beet leaves was measured in plants grown either daily irrigation or under water deficit using an infrared gas analyzer. All measurements were performed under controlled environmental conditions. In order to measure a consistent time series, data were scored with time intervals of 20s during 6h. Lyapunov exponents, fractal dimensions, KS entropy and relative LZ complexity were calculated. Stomatal conductance in both irrigated and non-irrigated plants was chaotic-like. Plants under water deficit showed a trend to a more complex behaviour, mainly in sunflower that showed better homeostasis than in sugar beet. Some biological implications are discussed.

scholarly journals The robustness of Brownie tag return models to complex spatiotemporal dynamics evaluated through simulation analysis

2017 ◽  
Vol 74 (11) ◽  
pp. 1845-1861 ◽  
Author(s):  
Matthew V. Lauretta ◽  
Daniel R. Goethel
Keyword(s):  
Experimental Design ◽  
Discrete Time ◽  
Temporal Dynamics ◽  
Simulation Analysis ◽  
Model Misspecification ◽  
Spatiotemporal Dynamics ◽  
Unbiased Estimation ◽  
Parameter Estimates ◽  
World Population ◽  
Spatiotemporal Model

The development of a reliable tagging program requires simulation testing the experimental design. However, the potential for model misspecification, particularly in the underlying spatiotemporal dynamics, is often ignored. A continuous time, spatially explicit, age-structured, capture–recapture operating model was developed to better emulate real-world population dynamics typically overlooked in spatially aggregated or discrete time tagging models. Various spatiotemporal model parametrizations, including case studies with Atlantic bluefin tuna (Thunnus thynnus) and yellowfin tuna (Thunnus albacares), were explored to evaluate the bias associated with Brownie tag return estimation models. Simulations demonstrated that accounting for connectivity was essential for obtaining unbiased parameter estimates and that migration rates could be reliably estimated without the correlation associated with other parameters (e.g., between tag reporting and mortality). Mortality parameter estimates were particularly sensitive to the temporal dynamics of the tagging and fishing seasons, but accounting for the seasonality in tag releases and fishery recaptures allowed for relatively unbiased estimation. Our results indicate that parameter bias and uncertainty can be severely underestimated when discrete time or spatially aggregated operating models are used to determine optimal experimental design of tagging studies.

scholarly journals A multi-scale eco-evolutionary model of cooperation reveals how microbial adaptation influences soil decomposition

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Elsa Abs ◽  
Hélène Leman ◽  
Régis Ferrière
Keyword(s):  
Temporal Dynamics ◽  
Global Environmental Change ◽  
Evolutionary Model ◽  
Terrestrial Ecosystems ◽  
Microbial Decomposition ◽  
Multi Scale ◽  
Spatio Temporal ◽  
The Stability ◽  
Micro Organisms ◽  
Som Decomposition

AbstractThe decomposition of soil organic matter (SOM) is a critical process in global terrestrial ecosystems. SOM decomposition is driven by micro-organisms that cooperate by secreting costly extracellular (exo-)enzymes. This raises a fundamental puzzle: the stability of microbial decomposition in spite of its evolutionary vulnerability to “cheaters”—mutant strains that reap the benefits of cooperation while paying a lower cost. Resolving this puzzle requires a multi-scale eco-evolutionary model that captures the spatio-temporal dynamics of molecule-molecule, molecule-cell, and cell-cell interactions. The analysis of such a model reveals local extinctions, microbial dispersal, and limited soil diffusivity as key factors of the evolutionary stability of microbial decomposition. At the scale of whole-ecosystem function, soil diffusivity influences the evolution of exo-enzyme production, which feeds back to the average SOM decomposition rate and stock. Microbial adaptive evolution may thus be an important factor in the response of soil carbon fluxes to global environmental change.

scholarly journals CiteWiz: A Tool for the Visualization of Scientific Citation Networks

2007 ◽  
Vol 6 (3) ◽  
pp. 215-232 ◽  
Author(s):  
Niklas Elmqvist ◽  
Philippas Tsigas
Keyword(s):  
User Study ◽  
Citation Networks ◽  
Graph Layout ◽  
Citation Data ◽  
Citation Database ◽  
Basic Force ◽  
Scientific Citation ◽  
The Individual ◽  
High Level ◽  
Level Analysis

We present CiteWiz, an extensible framework for visualization of scientific citation networks. The system is based on a taxonomy of citation database usage for researchers, and provides a timeline visualization for overviews and an influence visualization for detailed views. The timeline displays the general chronology and importance of authors and articles in a citation database, whereas the influence visualization is implemented using the Growing Polygons technique, suitably modified to the context of browsing citation data. Using the latter technique, hierarchies of articles with potentially very long citation chains can be graphically represented. The visualization is augmented with mechanisms for parent–child visualization and suitable interaction techniques for interacting with the view hierarchy and the individual articles in the dataset. We also provide an interactive concept map for keywords and co-authorship using a basic force-directed graph layout scheme. A formal user study indicates that CiteWiz is significantly more efficient than traditional database interfaces for high-level analysis tasks relating to influence and overviews, and equally efficient for low-level tasks such as finding a paper and correlating bibliographical data.

Dynamics of Premixed H2/CH4 Flames Under Near-Blowoff Conditions

2009 ◽  
Author(s):  
Qingguo Zhang ◽  
Santosh J. Shanbhogue ◽  
Tim Lieuwen
Keyword(s):  
Strain Rate ◽  
Temporal Dynamics ◽  
Complex Dynamics ◽  
Vortex Breakdown ◽  
Local Strain ◽  
Mie Scattering ◽  
Flame Stabilization ◽  
Local Extinction ◽  
Extinction Events ◽  
Extinction Strain Rate

Swirling flows are widely used in industrial burners and gas turbine combustors for flame stabilization. Several prior studies have shown that these flames exhibit complex dynamics under near-blowoff conditions, associated with local flamelet extinction and alteration in the vortex breakdown flow structure. These extinction events are apparently due to the local strain rate irregularly oscillating above and below the extinction strain rate values near the attachment point. In this work, global, temporally resolved and detailed spatial measurements were obtained of hydrogen/methane flames. Supporting calculations of extinction strain rates were also performed using detailed kinetics. It is shown that flames become unsteady (or local extinctions happen) at a nearly constant extinction strain rate for different hydrogen/methane mixtures. Based upon analysis of these results, it is suggested that classic Damkohler number correlations of blowoff are, in fact, correlations for the onset of local-extinction events, not blowoff itself. Corresponding Mie scattering imaging of near-blowoff flames also was used to characterize the spatio-temporal dynamics of holes along the flame that are associated with local extinction.

scholarly journals Sensitivity of a data-assimilation system for reconstructing three-dimensional cardiac electrical dynamics

2020 ◽  
Vol 378 (2173) ◽  
pp. 20190388 ◽  
Author(s):  
Matthew J. Hoffman ◽  
Elizabeth M. Cherry
Keyword(s):  
Data Assimilation ◽  
Temporal Dynamics ◽  
Complex Dynamics ◽  
Three Dimensional ◽  
Model Error ◽  
Model Parameters ◽  
Ensemble Spread ◽  
Spatio Temporal ◽  
System States ◽  
Parameter Values

Modelling of cardiac electrical behaviour has led to important mechanistic insights, but important challenges, including uncertainty in model formulations and parameter values, make it difficult to obtain quantitatively accurate results. An alternative approach is combining models with observations from experiments to produce a data-informed reconstruction of system states over time. Here, we extend our earlier data-assimilation studies using an ensemble Kalman filter to reconstruct a three-dimensional time series of states with complex spatio-temporal dynamics using only surface observations of voltage. We consider the effects of several algorithmic and model parameters on the accuracy of reconstructions of known scroll-wave truth states using synthetic observations. In particular, we study the algorithm’s sensitivity to parameters governing different parts of the process and its robustness to several model-error conditions. We find that the algorithm can achieve an acceptable level of error in many cases, with the weakest performance occurring for model-error cases and more extreme parameter regimes with more complex dynamics. Analysis of the poorest-performing cases indicates an initial decrease in error followed by an increase when the ensemble spread is reduced. Our results suggest avenues for further improvement through increasing ensemble spread by incorporating additive inflation or using a parameter or multi-model ensemble. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

scholarly journals The representational dynamics of task and object processing in humans

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Martin N Hebart ◽  
Brett B Bankson ◽  
Assaf Harel ◽  
Chris I Baker ◽  
Radoslaw M Cichy
Keyword(s):  
Temporal Dynamics ◽  
Complex Dynamics ◽  
Visual Object ◽  
Multivariate Techniques ◽  
Object Representations ◽  
Object Processing ◽  
Human Cortex ◽  
Spatial And Temporal Dynamics ◽  
Object Features ◽  
Higher Visual Areas

Despite the importance of an observer’s goals in determining how a visual object is categorized, surprisingly little is known about how humans process the task context in which objects occur and how it may interact with the processing of objects. Using magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI) and multivariate techniques, we studied the spatial and temporal dynamics of task and object processing. Our results reveal a sequence of separate but overlapping task-related processes spread across frontoparietal and occipitotemporal cortex. Task exhibited late effects on object processing by selectively enhancing task-relevant object features, with limited impact on the overall pattern of object representations. Combining MEG and fMRI data, we reveal a parallel rise in task-related signals throughout the cerebral cortex, with an increasing dominance of task over object representations from early to higher visual areas. Collectively, our results reveal the complex dynamics underlying task and object representations throughout human cortex.

scholarly journals The Representational Dynamics of Task and Object Processing in Humans

2017 ◽  
Author(s):  
M. N. Hebart ◽  
B. B. Bankson ◽  
A. Harel ◽  
C. I. Baker ◽  
R. M. Cichy
Keyword(s):  
Temporal Dynamics ◽  
Complex Dynamics ◽  
Visual Object ◽  
Multivariate Techniques ◽  
Object Representations ◽  
Object Processing ◽  
Human Cortex ◽  
Spatial And Temporal Dynamics ◽  
Object Features ◽  
Higher Visual Areas

AbstractDespite the importance of an observer’s goals in determining how a visual object is categorized, surprisingly little is known about how humans process the task context in which objects occur and how it may interact with the processing of objects. Using magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI) and multivariate techniques, we studied the spatial and temporal dynamics of task and object processing. Our results reveal a sequence of separate but overlapping task-related processes spread across frontoparietal and occipitotemporal cortex. Task exhibited late effects on object processing by selectively enhancing task-relevant object features, with limited impact on the overall pattern of object representations. Combining MEG and fMRI data, we reveal a parallel rise in task-related signals throughout the cerebral cortex, with an increasing dominance of task over object representations from early to higher visual areas. Collectively, our results reveal the complex dynamics underlying task and object representations throughout human cortex.

scholarly journals Games and network structures on corruption, income inequality, and tax control

2020 ◽  
Vol 13 ◽  
pp. 152-172
Author(s):  
Elena Gubar ◽  
◽  
Edgar J. Sánchez Carrera ◽  
Suriya Kumacheva ◽  
Ekaterina Zhitkova ◽  
...  
Keyword(s):  
Simulation Analysis ◽  
Research Work ◽  
Evolutionary Model ◽  
Real Income ◽  
Public Authorities ◽  
Tax System ◽  
The Poor ◽  
Corrupt Behavior ◽  
And Control ◽  
The Impact

The income tax system is the main instrument of fiscal policy that aims to improve income distribution and economic growth, but the problem arises when there is corrupt behavior in that system. While the tax audit is a tax control tool that is costly, the tax system should guarantee, however, the instruments for tax collection. In this research work, we formulate a model in which all taxpayers decide to pay taxes or not according to their personal income, individual preferences with respect to the audit and tax control information perceived in their social environment. We develop a theoretical model to study the structure of citizen networks that must pay taxes. First, we assume that citizens are classified by two social groups, the rich and the poor. When all citizens are taxpayers, but public authorities are corrupt, we show that the poor group is the most affected by corruption. However, when taxpayers are corrupt or tax evaders, we implement mechanisms to audit and control this corrupt behavior. Hence, we show that this situation of corruption and control of tax payments can be represented by several well-known theoretical games. Then, we apply the evolutionary theory of the game in the network considering that each taxpayer receives information from his∖her neighbors about the probability of audit and that he∖she could react according to his∖her risk status and real income. Such behavior forms a group of informed agents that propagate the information beyond the proportions of the informed and uninformed contributors that are modified. Our evolutionary model in the structure of the network describes the changes in the population of taxpayers driven by the impact of information on the future fiscal audit. Our simulation analysis shows that the initial and final preferences of taxpayers depend on important parameters, that is, taxes and fines, audit information and costs.

scholarly journals Explaining the “How” of Self-Esteem Development: The Self-Organizing Self-Esteem Model

2017 ◽  
Vol 21 (1) ◽  
pp. 49-68 ◽  
Author(s):  
Naomi M. P. De Ruiter ◽  
Paul L. C. Van Geert ◽  
E. Saskia Kunnen
Keyword(s):  
Complex Dynamics ◽  
Traditional Approach ◽  
Underlying Mechanism ◽  
Self Esteem ◽  
The Self ◽  
Integrative Framework ◽  
Systems Perspective ◽  
Current Article ◽  
Self Organizing

The current article proposes a theoretical model of self-esteem called the Self-Organizing Self-Esteem (SOSE) model. The model provides an integrative framework for conceptualizing and understanding the intrinsic dynamics of self-esteem and the role of the context across 3 levels of development: The macro level, which is the level of trait self-esteem, the meso level, on which we find state self-esteem, and the micro level, which is the level of discrete self experiences. The model applies principles from the complex dynamics systems perspective to self-esteem, and can thus uniquely describe the underlying mechanism of self-esteem development based on self-organizational processes and interacting time scales. We compare the proposed SOSE model with a formalized account of the traditional approach to self-esteem, showing that the SOSE model is especially conducive to the understanding of self-esteem development in a way that the traditional approach is not—namely, in its ability to explain and predict the underlying dynamics of trait and state self-esteem, the meaning of variability, and the role of the context.

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