scholarly journals Shock generation comparison with planar and hemispherical targets in shock ignition relevant experiment

2017 ◽  
Vol 24 (9) ◽  
pp. 092708 ◽  
Author(s):  
S. D. Baton ◽  
E. Le Bel ◽  
S. Brygoo ◽  
X. Ribeyre ◽  
C. Rousseaux ◽  
...  
Keyword(s):  
Relevant Experiment ◽  
Shock Generation ◽  
Shock Ignition

scholarly journals Spherical strong-shock generation for shock-ignition inertial fusiona)

2015 ◽  
Vol 22 (5) ◽  
pp. 056310 ◽  
Author(s):  
W. Theobald ◽  
R. Nora ◽  
W. Seka ◽  
M. Lafon ◽  
K. S. Anderson ◽  
...  
Keyword(s):  
Strong Shock ◽  
Shock Generation ◽  
Shock Ignition

scholarly journals Enhanced hot-electron production and strong-shock generation in hydrogen-rich ablators for shock ignition

2017 ◽  
Vol 24 (12) ◽  
pp. 120702 ◽  
Author(s):  
W. Theobald ◽  
A. Bose ◽  
R. Yan ◽  
R. Betti ◽  
M. Lafon ◽  
...  
Keyword(s):  
Strong Shock ◽  
Hot Electron ◽  
Electron Production ◽  
Shock Generation ◽  
Shock Ignition

scholarly journals Inflationary stimulated Raman scattering in shock-ignition plasmas

2020 ◽  
Vol 27 (12) ◽  
pp. 122705
Author(s):  
S. J. Spencer ◽  
A. G. Seaton ◽  
T. Goffrey ◽  
T. D. Arber
Keyword(s):  
Raman Scattering ◽  
Stimulated Raman Scattering ◽  
Stimulated Raman ◽  
Shock Ignition

Optimal design of shock ignition target in order to stop generated hot electrons within the cold fuel

2021 ◽  
Author(s):  
Seddigheh Pourhosseini ◽  
Abbas Ghasemizad ◽  
Somayeh Rezaei ◽  
Mohammad J. Jafari
Keyword(s):  
Optimal Design ◽  
Hot Electrons ◽  
Shock Ignition

scholarly journals A Preliminary Study on Shock Ignition

1956 ◽  
Vol 4 (32) ◽  
pp. 223-230
Author(s):  
Ryuma KAWAMURA ◽  
Saburo YUI
Keyword(s):  
Preliminary Study ◽  
Shock Ignition

scholarly journals Laser plasma physics in shock ignition – transition from collisional to collisionless absorption

2013 ◽  
Vol 59 ◽  
pp. 05008 ◽  
Author(s):  
O. Klimo ◽  
V. T. Tikhonchuk ◽  
X. Ribeyre ◽  
G. Schurtz ◽  
C. Riconda ◽  
...  
Keyword(s):  
Plasma Physics ◽  
Laser Plasma ◽  
Shock Ignition

Finite Mach number spherical shock wave, application to shock ignition

2013 ◽  
Vol 20 (8) ◽  
pp. 082702 ◽  
Author(s):  
A. Vallet ◽  
X. Ribeyre ◽  
V. Tikhonchuk
Keyword(s):  
Shock Wave ◽  
Mach Number ◽  
Spherical Shock Wave ◽  
Spherical Shock ◽  
Shock Ignition

scholarly journals Progress in understanding the role of hot electrons for the shock ignition approach to inertial confinement fusion

2018 ◽  
Vol 59 (3) ◽  
pp. 032012 ◽  
Author(s):  
D. Batani ◽  
L. Antonelli ◽  
F. Barbato ◽  
G. Boutoux ◽  
A. Colaïtis ◽  
...  
Keyword(s):  
Inertial Confinement Fusion ◽  
Hot Electrons ◽  
Inertial Confinement ◽  
Confinement Fusion ◽  
Shock Ignition

scholarly journals Semi-analytical calculation of fuel parameters for shock ignition fusion

2017 ◽  
Vol 16 (4) ◽  
pp. 345-350
Author(s):  
S A Ghasemi ◽  
A H Farahbod ◽  
S Sobhanian ◽  
◽  
◽  
...  
Keyword(s):  
Analytical Calculation ◽  
Shock Ignition

Identification of Important Proteins in Protein Interaction Network Based on SVM

2014 ◽  
Vol 644-650 ◽  
pp. 5202-5206
Author(s):  
Yan Li Zha ◽  
Wan Cheng Luo
Keyword(s):  
Protein Interaction ◽  
Interaction Network ◽  
Biological Information ◽  
Yeast Protein ◽  
Essential Proteins ◽  
Svm Model ◽  
Network Topologies ◽  
Living Organisms ◽  
Yeast Protein Interaction ◽  
Relevant Experiment

Importance of proteins are different to perform functions of cells in living organisms according to the relevant experiment results, and more essential proteins is the most important kind of proteins. There are recently many computational approaches proposed to predict essential proteins in network level through network topologies combined with biological information of proteins. However it is still hard to identify them because of limitations of topological centralities and bioinformatic sources. And more it is the challenge is to perform better with less resources. Therefore in this paper, we first examine the correlation between common topological centralities and essential proteins and choose a few particular centralities, and then to build a SVM model, names as TC-SVM, for predicting the essential proteins. The new method has been applied to a yeast protein interaction networks, which are obtained from the BioGRID database. The ten folds experimental results show that the performance of predicting essential proteins by TC-SVM is excellent.

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