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The model described in this paper is a robust tool that will enable quantitative predictions of cold spray impacts and help further optimise the process.
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Regional and local scale modelling efforts in areas where higher resolution bathymetry exists (i.e. the U.S. and Australian continental shelves) will reduce overprediction, resulting in more accurate predictions of cold-water coral distribution.
We present an Artificial Neural Network based model for the prediction of cold rolling textures of steels.
In order to validate the hypothesis of prediction of cold worked material properties after flow forming, a few flow forming experiments are carried out.
Experimental and numerical investigations on the characterisation and prediction of cold formability of a ferritic steel sheet were performed in this study.
Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items.
Accounting for population structure had a more dramatic effect on prediction of cold injury, for which the unadjusted model accounted for 41.7% of variation, whereas the adjusted model accounted for only 22.5%.
This paper discusses the effect of rounded corners in the capacity prediction of cold-formed steel members.
New design procedures are recommended for strength prediction of cold-formed steel angle columns with fixed end boundary conditions.
Although ultimate strength prediction of cold-formed steel members is generally well addressed in design codes, pre- and post-peak member stiffness is only partially addressed; while member ductility and post-peak moment-rotation response suffers from a lack of any clear guidance.
The composite entropy change (S*) shows effect of both temperature and duration and characterizes cold shock intensity, and proposed prediction model of cold shock preservation quality can predict preservation quality of cucumber cold shocked at different temperatures and durations well.
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