scholarly journals Dynamic susceptibility of supercooled water and its relation to the dynamic crossover phenomenon

2009 ◽  
Vol 79 (4) ◽  
Author(s):  
Yang Zhang ◽  
Marco Lagi ◽  
Emiliano Fratini ◽  
Piero Baglioni ◽  
Eugene Mamontov ◽  
...  
Keyword(s):  
Supercooled Water ◽  
Dynamic Susceptibility ◽  
Dynamic Crossover

The Dynamic Response FunctionχT(Q,t) of Confined Supercooled Water and its Relation to the Dynamic Crossover Phenomenon

2010 ◽  
Vol 224 (1-2) ◽  
pp. 109-131 ◽  
Author(s):  
Sow-Hsin Chen ◽  
Yang Zhang ◽  
Marco Lagi ◽  
X. Chu ◽  
Li Liu ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Supercooled Water ◽  
Dynamic Crossover

Single particle dynamics of supercooled water and the kinetic glass transition

1998 ◽  
Vol 08 (PR6) ◽  
pp. Pr6-109-Pr6-113
Author(s):  
P. Gallo ◽  
F. Sciortino ◽  
P. Tartaglia ◽  
S.-H. Chen
Keyword(s):  
Glass Transition ◽  
Single Particle ◽  
Supercooled Water ◽  
Particle Dynamics

IMPROVED LUMPED-DIFFERENTIAL ANALYSIS OF THE FREEZING PROCESS IN A SUPERCOOLED WATER DROPLET

2020 ◽  
Author(s):  
Emerson Barbosa dos Anjos ◽  
Carolina Palma Naveira Cotta ◽  
Renato Machado Cotta ◽  
Igor Soares Carvalho ◽  
Manish Tiwari
Keyword(s):  
Water Droplet ◽  
Supercooled Water ◽  
Freezing Process ◽  
Differential Analysis

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

2020 ◽  
Vol 15 (4) ◽  
pp. 287-299
Author(s):  
Jie Zhang ◽  
Junhong Feng ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Brain Networks ◽  
Discrete Particle Swarm Optimization ◽  
Mri Brain ◽  
Mutation Operators ◽  
Neural Unit ◽  
Crossover And Mutation ◽  
The Brain ◽  
Index Table ◽  
Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

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