Linear discrete models with different time scales

1995 ◽  
Vol 43 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Eva Sánchez ◽  
Rafael Bravo de la Parra ◽  
Pierre Auger
Keyword(s):  
Time Scales ◽  
Discrete Models ◽  
Different Time Scales

scholarly journals Applications de méthodes d’agrégation de variables à l’analyse de modèles spatiaux de dynamique des populations

2008 ◽  
Vol Volume 9, 2007 Conference in... ◽  
Author(s):  
Tri Nguyen-Huu ◽  
Pierre Auger
Keyword(s):  
Population Dynamics ◽  
Time Scales ◽  
Discrete Models ◽  
Global Dynamics ◽  
Dynamique Des Populations ◽  
Simplified Models ◽  
Aggregation Of Variables ◽  
Reduced Models ◽  
International Audience ◽  
Different Time Scales

International audience Models in population dynamics can deal with an important number of parameters and variables, which can make them difficult to analyse. Aggregation of variables allow reducing complexity of such models by building simplified models governing fewer variables by use of the existence of different time scales associated to the processes governing the whole system. Those reduced models allows analysing and describing the global dynamics of the system. We present those methods for time discrete models and illustrate their use for the study of spatial host-parasitoids models. Les modèles de dynamique de populations peuvent prendre en compte un nombre important de paramètres et de variables, ce qui les rend difficiles à analyser. Lorsqu’il existe des processus associés à deux échelles de temps différentes, une lente et une rapide, les méthodes d’agrégation de variables permettent de construire un modèle simplifié qui comporte un nombre plus faible de variables. Elles permettent ainsi d’analyser et de décrire un système de manière globale. Nous présentons ces méthodes dans le cas de modèles discrets, puis nous illustrons leur utilisation à l’aide de modèles hôte-parasitoïdes spatialisés.

Agent-Based Computational Model

2017 ◽  
Author(s):  
Joshua M. Epstein
Keyword(s):  
Computational Model ◽  
Bounded Rationality ◽  
Time Scales ◽  
Spatially Explicit ◽  
Agent Based ◽  
Affective Component ◽  
Elementary Type ◽  
Social Component ◽  
Different Time Scales

This part describes the agent-based and computational model for Agent_Zero and demonstrates its capacity for generative minimalism. It first explains the replicability of the model before offering an interpretation of the model by imagining a guerilla war like Vietnam, Afghanistan, or Iraq, where events transpire on a 2-D population of contiguous yellow patches. Each patch is occupied by a single stationary indigenous agent, which has two possible states: inactive and active. The discussion then turns to Agent_Zero's affective component and an elementary type of bounded rationality, as well as its social component, with particular emphasis on disposition, action, and pseudocode. Computational parables are then presented, including a parable relating to the slaughter of innocents through dispositional contagion. This part also shows how the model can capture three spatially explicit examples in which affect and probability change on different time scales.

Green markets integration in different time scales: A regional analysis

2021 ◽  
Vol 98 ◽  
pp. 105254 ◽  
Author(s):  
Christian Urom ◽  
Hela Mzoughi ◽  
Ilyes Abid ◽  
Mariem Brahim
Keyword(s):  
Time Scales ◽  
Regional Analysis ◽  
Different Time Scales

scholarly journals Measuring chromosome dynamics on different time scales using resolvases with varying half-lives

2005 ◽  
Vol 56 (4) ◽  
pp. 1049-1061 ◽  
Author(s):  
Richard A. Stein ◽  
Shuang Deng ◽  
N. Patrick Higgins
Keyword(s):  
Time Scales ◽  
Chromosome Dynamics ◽  
Different Time Scales

scholarly journals Reservoir ecological operation considering outflow variations across different time scales

2021 ◽  
Vol 125 ◽  
pp. 107582 ◽  
Author(s):  
Yibo Wang ◽  
Pan Liu ◽  
Ming Dou ◽  
He Li ◽  
Bo Ming ◽  
...  
Keyword(s):  
Time Scales ◽  
Different Time Scales

scholarly journals Stellar noise and planet detection. I. Oscillations, granulation and sun-like spots

2010 ◽  
Vol 6 (S276) ◽  
pp. 527-529
Author(s):  
Xavier Dumusque ◽  
Nuno C. Santos ◽  
Stéphane Udry ◽  
Cristophe Lovis ◽  
Xavier Bonfils
Keyword(s):  
Radial Velocity ◽  
Time Scales ◽  
High Precision ◽  
Habitable Zone ◽  
Planet Detection ◽  
Solar Type ◽  
Physical Phenomena ◽  
Instrumental Precision ◽  
Observational Strategy ◽  
Different Time Scales

AbstractSpectrographs like HARPS can now reach a sub-ms−1 precision in radial-velocity (RV) (Pepe & Lovis 2008). At this level of accuracy, we start to be confronted with stellar noise produced by 3 different physical phenomena: oscillations, granulation phenomena (granulation, meso- and super-granulation) and activity. On solar type stars, these 3 types of perturbation can induce ms−1 RV variation, but on different time scales: 3 to 15 minutes for oscillations, 15 minutes to 1.5 days for granulation phenomena and 10 to 50 days for activity. The high precision observational strategy used on HARPS, 1 measure per night of 15 minutes, on 10 consecutive days each month, is optimized, due to a long exposure time, to average out the noise coming from oscillations (Dumusque et al. 2011a) but not to reduce the noise coming from granulation and activity (Dumusque et al. 2011a and Dumusque et al. 2011b). The smallest planets found with this strategy (Mayor et al. 2009) seems to be at the limit of the actual observational strategy and not at the limit of the instrumental precision. To be able to find Earth mass planets in the habitable zone of solar-type stars (200 days for a K0 dwarf), new observational strategies, averaging out simultaneously all type of stellar noise, are required.

scholarly journals Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018

2021 ◽  
Vol 13 (4) ◽  
pp. 811
Author(s):  
Hao Liu ◽  
Zexun Wei
Keyword(s):  
Time Scales ◽  
Correlation Coefficients ◽  
Dominant Mode ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Ocean Data Assimilation ◽  
Multiple Datasets ◽  
Different Time Scales

The variability in sea surface salinity (SSS) on different time scales plays an important role in associated oceanic or climate processes. In this study, we compare the SSS on sub-annual, annual, and interannual time scales among ten datasets, including in situ-based and satellite-based SSS products over 2011–2018. Furthermore, the dominant mode on different time scales is compared using the empirical orthogonal function (EOF). Our results show that the largest spread of ten products occurs on the sub-annual time scale. High correlation coefficients (0.6~0.95) are found in the global mean annual and interannual SSSs between individual products and the ensemble mean. Furthermore, this study shows good agreement among the ten datasets in representing the dominant mode of SSS on the annual and interannual time scales. This analysis provides information on the consistency and discrepancy of datasets to guide future use, such as improvements to ocean data assimilation and the quality of satellite-based data.

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