Sediment Dispersion Modeling of a Non-Orthogonal Boundary Fitted Technique: Mangrove Reclamation to Protect Cliff Erosion in Bintuni Bay, Indonesia

2021 ◽  
Vol 114 (sp1) ◽  
Author(s):  
Muslim Muin
Keyword(s):  
Dispersion Modeling ◽  
Cliff Erosion

AIR DISPERSION MODELING OF VOCs EMITTED FROM MSW LANDFILLS

2006 ◽  
Vol 5 (4) ◽  
pp. 731-741
Author(s):  
Fatih Taspinar ◽  
Ertan Durmusoglu ◽  
Aykan Karademir
Keyword(s):  
Dispersion Modeling ◽  
Air Dispersion Modeling ◽  
Air Dispersion

scholarly journals An Empirical Bayes approach for the identification of long-range chromosomal interaction from Hi-C data

2021 ◽  
Vol 20 (1) ◽  
pp. 1-15
Author(s):  
Qi Zhang ◽  
Zheng Xu ◽  
Yutong Lai
Keyword(s):  
Long Range ◽  
Latent Variables ◽  
Empirical Bayes ◽  
Genome Structure ◽  
Peak Detection ◽  
Data Matrix ◽  
Dispersion Modeling ◽  
Empirical Bayes Approach ◽  
Over Dispersion ◽  
Bayes Approach

Abstract Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e., peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the “true” interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a Smoothed Expectation Maximization algorithm, and the empirical null based on the zero assumption. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can identify better peaks in terms of accuracy, biological interpretability, and the consistency across biological replicates. The source code is available on Github (https://github.com/QiZhangStat/EBHiC).

scholarly journals Radiation Dose Calculations for a Hypothetical Accident in Xianning Nuclear Power Plant

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Bo Cao ◽  
Junxiao Zheng ◽  
Yixue Chen
Keyword(s):  
Radiation Dose ◽  
Nuclear Power ◽  
Atmospheric Dispersion ◽  
Harmful Effect ◽  
Atmospheric Stability ◽  
Dispersion Modeling ◽  
Dose Calculations ◽  
Air Concentration ◽  
The Public ◽  
Ground Deposition

Atmospheric dispersion modeling and radiation dose calculations have been performed for a hypothetical AP1000 SGTR accident by HotSpot code 3.03. TEDE, the respiratory time-integrated air concentration, and the ground deposition are calculated for various atmospheric stability classes, Pasquill stability categories A–F with site-specific averaged meteorological conditions. The results indicate that the maximum plume centerline ground deposition value of1.2E+2 kBq/m2occurred at about 1.4 km and the maximum TEDE value of1.41E-05 Sv occurred at 1.4 km from the reactor. It is still far below the annual regulatory limits of 1 mSv for the public as set in IAEA Safety Report Series number 115. The released radionuclides might be transported to long distances but will not have any harmful effect on the public.

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