Modelling and validation of TBR Hydrodynamics: Local comparison between CFD and experiments

Fuel ◽  
2020 ◽  
Vol 277 ◽  
pp. 118244 ◽  
Author(s):  
Sebastián Uribe ◽  
Mohammed Al-Ani ◽  
Mario E. Cordero ◽  
Muthanna Al-Dahhan
Keyword(s):  
Local Comparison

Cortical and trabecular bone parameters from HR-Pqct images at the Tibia: a local comparison with synchrotron radiation micro-computed tomography

2013 ◽  
Author(s):  
Agnes Ostertag ◽  
Francoise Peyrin ◽  
Sylvie Fernandez ◽  
Jean-Denis Laredo ◽  
Vernejoul Marie-Christine De ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Synchrotron Radiation ◽  
Trabecular Bone ◽  
Micro Computed Tomography ◽  
Bone Parameters ◽  
Local Comparison

Local comparison of cup to disc ratio in right and left eyes based on fusion of color fundus images and OCT B-scans

2019 ◽  
Vol 51 ◽  
pp. 30-41 ◽  
Author(s):  
Marzieh Mokhtari ◽  
Hossein Rabbani ◽  
Alireza Mehri-Dehnavi ◽  
Raheleh Kafieh ◽  
Mohammad-Reza Akhlaghi ◽  
...  
Keyword(s):  
Fundus Images ◽  
Local Comparison ◽  
Color Fundus Images ◽  
Cup To Disc Ratio

Discovery of Emergent Sorting Behavior using Swarm Intelligence and Grid-Enabled Genetic Algorithms

2014 ◽  
pp. 249-271
Author(s):  
Dimitris Kalles ◽  
Alexis Kaporis ◽  
Vassiliki Mperoukli ◽  
Anthony Chatzinouskas
Keyword(s):  
Genetic Algorithms ◽  
Swarm Intelligence ◽  
Time Behavior ◽  
Repeated Application ◽  
Grid Infrastructure ◽  
Sorting Algorithms ◽  
Parameter Configuration ◽  
Local Comparison ◽  
Run Time

The authors in this chapter use simple local comparison and swap operators and demonstrate that their repeated application ends up in sorted sequences across a range of variants, most of which are also genetically evolved. They experimentally validate a square run-time behavior for emergent sorting, suggesting that not knowing in advance which direction to sort and allowing such direction to emerge imposes a n/logn penalty over conventional techniques. The authors validate the emergent sorting algorithms via genetically searching for the most favorable parameter configuration using a grid infrastructure.

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