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(i) Prediction error degrees of different kinds of meteorological elements should be estimated in the same manner.
Data provided in Scenarios 1 through 3 have been used for validating the G i prediction methods for dual-porosity reservoirs.
In the past, the authors of this paper have been working specifically on two problems: (i) prediction of drug induced toxicity and (ii) evaluation of the target drug chemical interaction based on chemical descriptors.
In this paper, we focus on reviewing the application of data mining and analytical techniques designed so far for (i) prediction, (ii) detection, and (iii) development of appropriate disaster management strategy based on the collected data from disasters.
In such a case, the same steps for G i prediction for a single-porosity system utilizing Eq. 31 would need to be taken except for replacing (bar{lambda }) with (bar{lambda }_{text{m}}).
Two alternative hypotheses flowing from RQ3 can be stated as follows: (i) Prediction activity interleaved with visualization will lead to higher rate of problem solving than simply viewing the visualization with parallel instructor commentary in a programming class (H1_3) and (ii) the "Prediction" group will show higher average post-test scores than the "Viewing" group (H1_4).
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For type (i) predictions the method is useful for proposing new target genes.
Type I predictions are usually obtained as backward deductions.
However, the argument in favor of active interaction is weaker here than for Type I predictions.
(A lot of this seems to depend on assertions that I made predictions I never actually made).
(i) The prediction quality can be further improved.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com