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The components with large TCTSF are the weak system parts.
Fresh feedstock contains a considerable amount of high-boiling-point heavy components with large molecular weight, which are easy to crack.
To prevent the resultant component degradation, it is indispensable to disconnect the closed circuit by inserting electrical insulation to isolate cathodic components with large surface areas, such as fuel assemblies, low-pressure heat exchangers and steam condensers.
In order to carry out high-precision machining of aerospace structural components with large size, thin wall and complex surface, this paper proposes a novel parallel kinematic machine (PKM) and formulates its semi-analytical theoretical stiffness model considering gravitational effects that is verified by stiffness experiments.
From a data reduction point of view, this is not always desirable if the aim is to find a parsimonious model for predicting the outcome, because sometimes principal components with large variances may have low correlations with the outcome [ 47].
This failure may reflect a bias introduced by the dataset, in which many samples were cell sorted to specifically select aneuploid cell fractions, or could reflect an inherent bias of the method towards more distinct components, which would tend to favor components with large amplifications.
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There are other methods for colored signals, such as explicitly matching components with larger inter-frequency correlations of signal envelopes [13 15].
The latter could potentially be replaced by a thinner, architectured ZTA layer, thereby allowing for larger acetabular components, with larger range of motion and lower dislocation risk.
In addition, analysis of the distilled components indicates that lightweight components with smaller carbon numbers are removed firstly by steam distillation, followed by heavier components with larger carbon numbers.
Such phenomenon becomes more prominent with the increase of q m, n / σ m, n SJR, and thus the DCT components with larger q m, n / σ m, n SJR would be more discriminative in SDJPEG detection.
Obviously, differences between the point estimates of variance components with larger and smaller effects will appear more pronounced on the variance scale than on the standard deviation scale (although obviously the upper and lower bounds of their intervals on the variance scale will be the square of those on the standard deviation scale too).
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