Management Node Selection Based on Cloud Model in a Distributed Network

2018 ◽  
pp. 176-185
Author(s):  
Hanyi Tang ◽  
Qibo Sun ◽  
Jinglin Li
Keyword(s):  
Cloud Model ◽  
Distributed Network ◽  
Node Selection

E-Government Public Cloud Model (EGPCM)

2018 ◽  
Vol 31 (1) ◽  
pp. 244
Author(s):  
Nada M. Al-Hakkak ◽  
Ban Salman Shukur ◽  
Atheel Sabih Shaker
Keyword(s):  
Cloud Computing ◽  
Cloud Model ◽  
Public Cloud ◽  
New Model ◽  
The World

   The concept of implementing e-government systems is growing widely all around the world and becoming an interest to all governments. However, governments are still seeking for effective ways to implement e-government systems properly and successfully. As services of e-government increased and citizens’ demands expand, the e-government systems become more costly to satisfy the growing needs. The cloud computing is a technique that has been discussed lately as a solution to overcome some problems that an e-government implementation or expansion is going through. This paper is a proposal of a  new model for e-government on basis of cloud computing. E-Government Public Cloud Model EGPCM, for e-government is related to public cloud computing.

Image scrambling algorithm based on cloud model

2013 ◽  
Vol 33 (9) ◽  
pp. 2497-2500
Author(s):  
Tiesheng FAN ◽  
Zhongqing ZHANG ◽  
Jing SUN ◽  
Xuechun LUO ◽  
Guiqiang LU ◽  
...  
Keyword(s):  
Cloud Model ◽  
Image Scrambling

Evaluation of Landslide Hazard Degree Based On the Normal Cloud Model

2017 ◽  
Vol 10 (01) ◽  
pp. 88-94
Author(s):  
CHEN DONGHUI ◽  
XU PEIHUA ◽  
ZHANG WEN ◽  
CHEN JIANPING ◽  
SONG SHENGYUAN ◽  
...  
Keyword(s):  
Cloud Model ◽  
Landslide Hazard ◽  
Hazard Degree

scholarly journals A Cloud Microphysics Parameterization for Shallow Cumulus Clouds Based on Lagrangian Cloud Model Simulations

2018 ◽  
Vol 75 (11) ◽  
pp. 4031-4047 ◽  
Author(s):  
Yign Noh ◽  
Donggun Oh ◽  
Fabian Hoffmann ◽  
Siegfried Raasch
Keyword(s):  
Cloud Model ◽  
Cloud Microphysics ◽  
Heaviside Function ◽  
Mixing Ratio ◽  
Cumulus Clouds ◽  
Cloud Droplets ◽  
Shallow Cumulus ◽  
Initial Stage ◽  
Accretion Rates ◽  
The Moment

Abstract Cloud microphysics parameterizations for shallow cumulus clouds are analyzed based on Lagrangian cloud model (LCM) data, focusing on autoconversion and accretion. The autoconversion and accretion rates, A and C, respectively, are calculated directly by capturing the moment of the conversion of individual Lagrangian droplets from cloud droplets to raindrops, and it results in the reproduction of the formulas of A and C for the first time. Comparison with various parameterizations reveals the closest agreement with Tripoli and Cotton, such as and , where and are the mixing ratio and the number concentration of cloud droplets, is the mixing ratio of raindrops, is the threshold volume radius, and H is the Heaviside function. Furthermore, it is found that increases linearly with the dissipation rate and the standard deviation of radius and that decreases rapidly with while disappearing at > 3.5 μm. The LCM also reveals that and increase with time during the period of autoconversion, which helps to suppress the early precipitation by reducing A with smaller and larger in the initial stage. Finally, is found to be affected by the accumulated collisional growth, which determines the drop size distribution.

scholarly journals Evaluation of freeze–thaw erosion in Tibet based on the cloud model

2021 ◽  
Author(s):  
Junfu Fan ◽  
Taoying Hu ◽  
Xiao Yu ◽  
Jiahao Chen ◽  
Liusheng Han ◽  
...  
Keyword(s):  
Cloud Model ◽  
Freeze Thaw
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