New Model for Gas Well Loading Prediction

2009 ◽  
Author(s):  
Desheng Zhou ◽  
Hong Yuan
Keyword(s):  
Gas Well ◽  
New Model

A New Model for Predicting Gas-Well Liquid Loading

2010 ◽  
Vol 25 (02) ◽  
pp. 172-181 ◽  
Author(s):  
Desheng Zhou ◽  
Hong Yuan
Keyword(s):  
Liquid Loading ◽  
Gas Well ◽  
New Model

A New Model to Predict Average Pressure Difference of Liquid Droplet in Gas Well

2013 ◽  
Author(s):  
Zhong Hai-Quan ◽  
Liu Zhong-Neng ◽  
Liu Tong ◽  
Liang Kai ◽  
Ren Yong
Keyword(s):  
Pressure Difference ◽  
Liquid Droplet ◽  
Average Pressure ◽  
Gas Well ◽  
New Model

A new model for predicting liquid loading in a gas well

2015 ◽  
Vol 26 ◽  
pp. 1530-1541 ◽  
Author(s):  
Adesina Fadairo ◽  
Falode Olugbenga ◽  
Nwosu Chioma Sylvia
Keyword(s):  
Liquid Loading ◽  
Gas Well ◽  
New Model

The Hitachi Model HS-8 Electron Microscope

1967 ◽  
Vol 25 ◽  
pp. 238-239
Author(s):  
H. Akabori ◽  
K. Nishiwaki ◽  
K. Yoneta
Keyword(s):  
Electron Microscope ◽  
New Model ◽  
Predecessor Model

By improving the predecessor Model HS- 7 electron microscope for the purpose of easier operation, we have recently completed new Model HS-8 electron microscope featuring higher performance and ease of operation.

516: A Simplified Method to Estimate the Absolute Renal Uptake of 99MTC-DMSA: Evaluation with a New Model using the Radioactivity of Nephrectomy Specimens as Reference

2005 ◽  
Vol 173 (4S) ◽  
pp. 140-141
Author(s):  
Mariana Lima ◽  
Celso D. Ramos ◽  
Sérgio Q. Brunetto ◽  
Marcelo Lopes de Lima ◽  
Carla R.M. Sansana ◽  
...  
Keyword(s):  
New Model ◽  
Renal Uptake ◽  
Simplified Method ◽  
The Absolute

Analyzing Stochastic Dependence of Cognitive Processes in Multidimensional Source Recognition

2014 ◽  
Vol 61 (5) ◽  
pp. 402-415 ◽  
Author(s):  
Thorsten Meiser
Keyword(s):  
Cognitive Processes ◽  
Model Complexity ◽  
Multinomial Model ◽  
Stochastic Dependence ◽  
New Model ◽  
Multinomial Processing Tree ◽  
Cognitive States ◽  
The Family ◽  
Tree Models ◽  
Flexible Framework

Stochastic dependence among cognitive processes can be modeled in different ways, and the family of multinomial processing tree models provides a flexible framework for analyzing stochastic dependence among discrete cognitive states. This article presents a multinomial model of multidimensional source recognition that specifies stochastic dependence by a parameter for the joint retrieval of multiple source attributes together with parameters for stochastically independent retrieval. The new model is equivalent to a previous multinomial model of multidimensional source memory for a subset of the parameter space. An empirical application illustrates the advantages of the new multinomial model of joint source recognition. The new model allows for a direct comparison of joint source retrieval across conditions, it avoids statistical problems due to inflated confidence intervals and does not imply a conceptual imbalance between source dimensions. Model selection criteria that take model complexity into account corroborate the new model of joint source recognition.

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