scholarly journals Large Data Throughput Optimization Model with Full C order model Parallel Flow Number Prediction Optical Domain

2016 ◽  
Vol 14 (2A) ◽  
pp. 10
Author(s):  
Hao Yang ◽  
Jianan Zhao ◽  
Wenqing Zheng ◽  
Jianguo Yu
Keyword(s):  
Optimization Model ◽  
Large Data ◽  
Parallel Flow ◽  
Throughput Optimization ◽  
Order Model ◽  
Flow Number ◽  
Data Throughput ◽  
Optical Domain

scholarly journals Energy-aware data throughput optimization for next generation internet

2019 ◽  
Vol 476 ◽  
pp. 413-428
Author(s):  
Tevfik Kosar ◽  
Ismail Alan ◽  
M. Fatih Bulut
Keyword(s):  
Throughput Optimization ◽  
Next Generation ◽  
Energy Aware ◽  
Next Generation Internet ◽  
Data Throughput

Data Throughput Optimization for Vehicle to Infrastructure Communications

2018 ◽  
pp. 93-101
Author(s):  
Angela Sara Cacciapuoti ◽  
Marcello Caleffi ◽  
Adriano Masone ◽  
Antonio Sforza ◽  
Claudio Sterle
Keyword(s):  
Throughput Optimization ◽  
Data Throughput ◽  
Vehicle To Infrastructure

scholarly journals Discriminating Between Second-Order Model With/Without Interaction Base on Central Tendency Estimation

2021 ◽  
Vol 4 (3) ◽  
pp. 47-63
Author(s):  
Owhondah P.S. ◽  
Enegesele D. ◽  
Biu O.E. ◽  
Wokoma D.S.A.
Keyword(s):  
Regression Model ◽  
Sample Size ◽  
Large Data ◽  
Second Order ◽  
Data Sets ◽  
Large Sample Size ◽  
Order Model ◽  
Data Set ◽  
The Mean ◽  
Better Than

The study deals with discriminating between the second-order models with/without interaction on central tendency estimation using the ordinary least square (OLS) method for the estimation of the model parameters. The paper considered two different sets of data (small and large) sample size. The small sample size used data of unemployment rate as a response, inflation rate and exchange rate as the predictors from 2007 to 2018 and the large sample size was data of flow-rate on hydrate formation for Niger Delta deep offshore field. The〖 R〗^2, AIC, SBC, and SSE were computed for both data sets to test for adequacy of the models. The results show that all three models are similar for smaller data set while for large data set the second-order model centered on the median with/without interaction is the best base on the number of significant parameters. The model’s selection criterion values (R^2, AIC, SBC, and SSE) were found to be equal for models centered on median and mode for both large and small data sets. However, the model centered on median and mode with/without interaction were better than the model centered on the mean for large data sets. This study shows that the second-order regression model centered on median and mode are better than the model centered on the mean for large data set, while they are similar for smaller data set. Hence, the second-order regression model centered on median and mode with or without interaction are better than the second-order regression model centered on the mean.

The Infancy of Solar-Type Stars and its Relevance for the Origin of Life

1997 ◽  
Vol 161 ◽  
pp. 267-282 ◽  
Author(s):  
Thierry Montmerle
Keyword(s):  
Main Sequence ◽  
Solar Nebula ◽  
Circumstellar Disks ◽  
T Tauri Stars ◽  
Necessary Condition ◽  
Sequence Evolution ◽  
T Tauri ◽  
Solar Type ◽  
Optical Domain ◽  
The Origin Of Life

AbstractFor life to develop, planets are a necessary condition. Likewise, for planets to form, stars must be surrounded by circumstellar disks, at least some time during their pre-main sequence evolution. Much progress has been made recently in the study of young solar-like stars. In the optical domain, these stars are known as «T Tauri stars». A significant number show IR excess, and other phenomena indirectly suggesting the presence of circumstellar disks. The current wisdom is that there is an evolutionary sequence from protostars to T Tauri stars. This sequence is characterized by the initial presence of disks, with lifetimes ~ 1-10 Myr after the intial collapse of a dense envelope having given birth to a star. While they are present, about 30% of the disks have masses larger than the minimum solar nebula. Their disappearance may correspond to the growth of dust grains, followed by planetesimal and planet formation, but this is not yet demonstrated.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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