Distributed parameter identification for a label-structured cell population dynamics model using CFSE histogram time-series data

2008 ◽  
Vol 59 (5) ◽  
pp. 581-603 ◽  
Author(s):  
Tatyana Luzyanina ◽  
Dirk Roose ◽  
Gennady Bocharov
2019 ◽  
Vol 16 (4) ◽  
pp. 3018-3046
Author(s):  
Frédérique Clément ◽  
◽  
Béatrice Laroche ◽  
Frédérique Robin ◽  
◽  
...  

2019 ◽  
Vol 37 (4) ◽  
pp. 461-468 ◽  
Author(s):  
David S. Fischer ◽  
Anna K. Fiedler ◽  
Eric M. Kernfeld ◽  
Ryan M. J. Genga ◽  
Aimée Bastidas-Ponce ◽  
...  

Ecosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
Author(s):  
Brenda J. Hanley ◽  
André A. Dhondt ◽  
Brian Dennis ◽  
Krysten L. Schuler

2020 ◽  
Author(s):  
Hung Chak Ho ◽  
Guangqing Chi

Abstract. Land vulnerability and development can be restricted by both land policy and geophysical limits. Land vulnerability and development cannot be simply quantified by land cover/use change, because growth related to population dynamics is not horizontal. Particularly, time-series data with a higher flexibility considering the ability of land to be developed should be used to identify areas of spatiotemporal change. By considering the policy aspects of land development, this approach will allow one to further identify the lands facing population stress, socioeconomic burdens, and health risks. Here the concept of “land developability” is expanded to include policy-driven factors and land vulnerability to better reconcile developability with socio-environmental justice. The first phrase of policy-driven land developability mapping is implemented in estimating land information across the contiguous United States in 2001, 2006, and 2011. Multiscale data products for state-, county- and census-tract-levels are provided from this estimation. The extension of this approach can be applied to other countries with modifications for their specific scenarios. The data generated from this work are available at https://doi.org/10.7910/DVN/AMZMWH (Chi and Ho, 2019).


PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e106228 ◽  
Author(s):  
Marek Brabec ◽  
Alois Honěk ◽  
Stano Pekár ◽  
Zdenka Martinková

Author(s):  
Soumik Sarkar ◽  
Kushal Mukherjee ◽  
Xin Jin ◽  
Asok Ray

This paper presents a data-driven method of parameter identification in nonlinear systems based on the theories of symbolic dynamics. Although construction of finite-state-machine models from symbol sequences has been widely reported, similar efforts have not been expended to investigate partitioning of time series data to optimally generate symbol sequences. A data-set partitioning procedure is proposed to extract features from time series data by optimizing a multi-objective cost functional. Performance of the optimal partitioning procedure is compared with those of other traditional partitioning (e.g., uniform and maximum entropy) schemes. Then, tools of pattern classification are applied to identify the ranges of multiple parameters of a well-known chaotic nonlinear dynamical system, namely the Duffing Equation, from its time series response.


Sign in / Sign up

Export Citation Format

Share Document