scholarly journals SARS‐CoV‐2 spike evolutionary behaviors; simulation of N501Y mutation outcomes in terms of immunogenicity and structural characteristic

2021 ◽  
Author(s):  
Neda Rostami ◽  
Edris Choupani ◽  
Yaeren Hernandez ◽  
Seyed S. Arab ◽  
Seyed M. Jazayeri ◽  
...  
Keyword(s):  
Structural Characteristic

Academic knowledge production and prefigurative politics

2021 ◽  
Vol 2021 (267-268) ◽  
pp. 163-167
Author(s):  
Beatriz P. Lorente
Keyword(s):  
Peer Review ◽  
Structural Characteristic ◽  
Knowledge Production ◽  
Review Process ◽  
Peer Review Process ◽  
Academic Knowledge ◽  
Means And Ends ◽  
Sociology Of Language ◽  
Prefigurative Politics ◽  
Access Model

Abstract Inequality is the pervasive structural characteristic of academic knowledge production. To dismantle this inequality, the challenge raised by prefigurative politics which is based on an ethos of congruence between means and ends must be taken up by the International Journal of the Sociology of Language. The IJSL’s peer review process, its academic conventions and its access model can potentially be spaces for concrete practices that prefigure parity in academic knowledge production.

scholarly journals Block Compressed Sensing of Images Using Adaptive Granular Reconstruction

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Ran Li ◽  
Hongbing Liu ◽  
Yu Zeng ◽  
Yanling Li
Keyword(s):  
Computational Complexity ◽  
Compressed Sensing ◽  
Structural Characteristic ◽  
Rate Distortion ◽  
Reconstructed Image ◽  
Hard Thresholding ◽  
Low Computational Complexity ◽  
Principle Components Analysis ◽  
Reconstruction Performance ◽  
Block Compressed Sensing

In the framework of block Compressed Sensing (CS), the reconstruction algorithm based on the Smoothed Projected Landweber (SPL) iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA) to perform the adaptive hard-thresholding shrinkage. However, during learning the PCA matrix, it affects the reconstruction performance of Landweber iteration to neglect the stationary local structural characteristic of image. To solve the above problem, this paper firstly uses the Granular Computing (GrC) to decompose an image into several granules depending on the structural features of patches. Then, we perform the PCA to learn the sparse representation basis corresponding to each granule. Finally, the hard-thresholding shrinkage is employed to remove the noises in patches. The patches in granule have the stationary local structural characteristic, so that our method can effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has better objective quality when compared with several traditional ones. The edge and texture details in the reconstructed image are better preserved, which guarantees the better visual quality. Besides, our method has still a low computational complexity of reconstruction.

scholarly journals Structural Characteristic of Intelligence and Embodiment in Two-Mobile Robots System.

2001 ◽  
Vol 67 (660) ◽  
pp. 2587-2594 ◽  
Author(s):  
Tsutomu SAWADA ◽  
Atsushi SUGAI ◽  
Sumiaki ICHIKAWA ◽  
Fumio HARA
Keyword(s):  
Mobile Robots ◽  
Structural Characteristic

Study of the Mechanism of Distortion on the Condition of Heating of Composite Bottom Pan (CBP) Based on ANSYS

2014 ◽  
Vol 933 ◽  
pp. 165-169
Author(s):  
Yun Hua Luo ◽  
Zhi Xiong Yan ◽  
Jia Lin Xiong
Keyword(s):  
Stainless Steel ◽  
Structural Characteristic ◽  
Thermal Deformation ◽  
Steel Sheet ◽  
Theoretical Foundation ◽  
Stainless Steel Sheet ◽  
Design And Optimization ◽  
Deformation Amount

As for the phenomenon of the bottom thermal inward convex distortion of the Composite Bottom Pan (CBP) on the condition of heating, the mechanism of the bottom distortion was researched on the basis of ANSYS 10.0 considering the structural characteristic and the heating conditions of the composite bottom pan. The results showed that the thicker the bottom of the composite bottom pan, the smaller its thermal deformation amount. The thicker the junction of the pot and wall was, the smaller its deformation. And it was helpful for reduction of deformation to use a big rounded transition. The increase of rings number of magnetic conductive sheet would lead to a less bottom thermal inward convex distortion of the CBP. The larger the area of the additional stainless steel sheet (or ring), the smaller the deformation amount. Consequently, the research laid a solid theoretical foundation for the design and optimization of the structure of composite bottom pan.

Structural Characteristic of One Dimensional Single Crystalline of InN Nanowires

2007 ◽  
Vol 44 (4) ◽  
pp. 202-207 ◽  
Author(s):  
Yun-Ki Byeun ◽  
Yong-Keun Chung ◽  
Sang-Hoon Lee ◽  
Sung-Churl Choi
Keyword(s):  
Structural Characteristic ◽  
Single Crystalline ◽  
One Dimensional

Multichannel absorption compensation with a data-driven structural regularization

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. V71-V80 ◽  
Author(s):  
Xiong Ma ◽  
Guofa Li ◽  
Hao Li ◽  
Wuyang Yang
Keyword(s):  
Cost Function ◽  
Structural Characteristic ◽  
Seismic Data ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Frequency Noise ◽  
Characteristic Operator ◽  
High Frequency Noise ◽  
The Cost

Seismic absorption compensation is an important processing approach to mitigate the attenuation effects caused by the intrinsic inelasticity of subsurface media and to enhance seismic resolution. However, conventional absorption compensation approaches ignore the spatial connection along seismic traces, which makes the compensation result vulnerable to high-frequency noise amplification, thus reducing the signal-to-noise ratio (S/N) of the result. To alleviate this issue, we have developed a structurally constrained multichannel absorption compensation (SC-MAC) algorithm. In the cost function of this algorithm, we exploit an [Formula: see text] norm to constrain the reflectivity series and an [Formula: see text] norm to regularize the reflection structural characteristic of the compensation data. The reflection structural characteristic operator, extracted from the observed stacked seismic data, is the core of the structural regularization term. We then solve the cost function of SC-MAC by the alternating direction method of multipliers. Benefiting from the introduction of reflection structure constraint, SC-MAC improves the stability of the compensation result and inhibits the amplification of high-frequency noise. Synthetic and field data examples demonstrate that our proposed method is more robust to random noise and can not only improve the resolution of seismic data, but also maintain the S/N of the compensation seismic data.

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