scholarly journals Multi-scale data-driven modeling and observation of calcium puffs

Cell Calcium ◽  
2012 ◽  
Vol 52 (2) ◽  
pp. 152-160 ◽  
Author(s):  
Ghanim Ullah ◽  
Ian Parker ◽  
Don-On Daniel Mak ◽  
John E. Pearson
Keyword(s):  
Data Driven ◽  
Multi Scale ◽  
Data Driven Modeling ◽  
Calcium Puffs ◽  
Scale Data

scholarly journals Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy

2020 ◽  
Vol 101 (3) ◽  
pp. 1583-1619
Author(s):  
Giuseppe Quaranta ◽  
Giovanni Formica ◽  
J. Tenreiro Machado ◽  
Walter Lacarbonara ◽  
Sami F. Masri
Keyword(s):  
Differential Evolution Algorithm ◽  
Northern Region ◽  
Multidimensional Analysis ◽  
Data Driven ◽  
Type Model ◽  
Epidemic Spreading ◽  
Epidemic Dynamics ◽  
Multi Scale ◽  
Evolution Algorithm ◽  
Scale Data

Abstract The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of a SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilarities of the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way toward a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.

scholarly journals Multi-scale Observation and Data-driven Modeling of Magnetic Reconnection in Flaring Chromospheric Plasmas on the Sun

2021 ◽  
Author(s):  
Xiaoli Yan ◽  
Zhike Xue ◽  
Chaowei Jiang ◽  
Eric Priest ◽  
Bernhard Kliem ◽  
...  
Keyword(s):  
Magnetic Reconnection ◽  
Solar Flares ◽  
Data Driven ◽  
Multi Scale ◽  
Radiation Spectra ◽  
Laboratory Plasmas ◽  
Astrophysical Objects ◽  
Multiple Wavelengths ◽  
Resolution Imaging ◽  
Data Driven Modeling

Abstract Magnetic reconnection is a multi-faceted process of energy conversion in astrophysical, space and laboratory plasmas that operates at microscopic scales but has macroscopic drivers and consequences. Solar flares present a key laboratory for its study, leaving imprints of the microscopic physics in radiation spectra and allowing the macroscopic evolution to be imaged, yet a full observational characterization remains elusive. Here we combine high resolution imaging and spectral observations of a solar flare at multiple wavelengths with data-driven magnetohydrodynamic modeling to study the dynamics of the involved plasma from the current sheet to the plasmoid scale. The flare resulted from the interaction of a twisted filament and chromospheric fibrils. By inferring the reconnection to be fast and mediated by plasmoids, the relevance of this reconnection mode is found to extend beyond hot flare plasmas to such cool structures in the chromosphere, which have many analogs in astrophysical objects.

scholarly journals Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy

2020 ◽  
Author(s):  
Giuseppe Quaranta ◽  
Giovanni Formica ◽  
J. Terneiro Machado ◽  
Walter Lacarbonara ◽  
Sami F. Masri
Keyword(s):  
Differential Evolution Algorithm ◽  
Northern Region ◽  
Multidimensional Analysis ◽  
Data Driven ◽  
Type Model ◽  
Epidemic Spreading ◽  
Epidemic Dynamics ◽  
Multi Scale ◽  
Evolution Algorithm ◽  
Scale Data

Abstract The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of an SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilaritiesof the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way towards a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.

Multi-scale data-driven engineering for biosynthetic titer improvement

2020 ◽  
Vol 65 ◽  
pp. 205-212 ◽  
Author(s):  
Zhixing Cao ◽  
Jiaming Yu ◽  
Weishan Wang ◽  
Hongzhong Lu ◽  
Xuekui Xia ◽  
...  
Keyword(s):  
Data Driven ◽  
Multi Scale ◽  
Scale Data

scholarly journals Atomistic-to-meso multi-scale data-driven graph surrogate modeling of dislocation glide

2021 ◽  
Vol 197 ◽  
pp. 110569
Author(s):  
Eduardo A. Barros de Moraes ◽  
Jorge L. Suzuki ◽  
Mohsen Zayernouri
Keyword(s):  
Surrogate Modeling ◽  
Data Driven ◽  
Dislocation Glide ◽  
Multi Scale ◽  
Scale Data

scholarly journals Are college campuses superspreaders? A data-driven modeling study

2021 ◽  
pp. 1-11
Author(s):  
Hannah Lu ◽  
Cortney Weintz ◽  
Joseph Pace ◽  
Dhiraj Indana ◽  
Kevin Linka ◽  
...  
Keyword(s):  
College Campuses ◽  
Data Driven ◽  
Data Driven Modeling ◽  
Modeling Study
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