The current research is concerned with studying the decisive answers which are considered quick and conclusive. These answers can effectively interrupt the opponent's argument and close the dialogue.This research is concentrated on deliberative methodology focusing on the decisive answer's activity and ending them through several completing and argument sides. This research consists of an introduction and three parts, the current introduction is focused the light on the concept of decisive answers and its uses in literature and the scarce of speech, and how to consider it with one dialogue description,that dialogue constitute by ? The first part is concerned with those answers through the deliberative methodology and classifying decisive answers in sequence with those answers. Part two is dealt with arrangement and employment of arguments in decisive answers in consist with argument concept, it is studied the mechanism of presenting arguments in this field. The last and the third partis dealt with the origins and the essence of decisive answers through critical necessities that arguments are concentrated through.In this research, there is a concentration in dialogue necessities and the mechanism of intentions for the basis of those answers.
The Decisive Answers: A Pragmatic Linguistic Study
Pragmatic Linguistics
discourse analysis
illocutionary act
Argument
Publication Date
Mon Nov 11 2019
Journal Name
Spe
Modeling Rate of Penetration using Artificial Intelligent System and Multiple Regression Analysis
Abstract<p>Over the years, the prediction of penetration rate (ROP) has played a key rule for drilling engineers due it is effect on the optimization of various parameters that related to substantial cost saving. Many researchers have continually worked to optimize penetration rate. A major issue with most published studies is that there is no simple model currently available to guarantee the ROP prediction.</p><p>The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE Iraq was conducted to predict the ROP from a large number of parame</p>
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neural network
strength
machine learning
ROP prediction
Upstream Oil & Gas
prediction
ANN model
input parameter
multiple regression analysis
artificial neural network
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