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Data quality (DQ) is the practice of assessing, improving, and maintaining the condition of data with respect to established specifications or requirements and is an important component of IQ.
New methods should also be explored simultaneously to evaluate the risk of hidden failure under the condition of data deficiency.
In the case that we could control the condition of data collection such as illumination and surface condition of sediments for digital image scanning and RSC measurement, these gamma values could be calibrated and defined well before data collection.
Although comparison between cases with and without missing data did not show statistically significant differences for cases characteristics, we cannot rule out that the condition of data missing completely at random does not hold.
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On the whole, 112 experiments have been carried out in different working conditions of data centers.
Any firm that thinks it's sufficient to simply provide disclosures in an end-user licensing agreement or present the terms and conditions of data use at sign-up is missing the point.
Comprehensive numerical studies of thermal management of data centers were presented by several investigators for different geometric and operating conditions of data centers.
For solving this problem, firstly, the fluency conditions of data flow for selected configurations of the track for data registration needs were formulated.
In the experiment, we could observe that the adaptive activation model saves 30 50% of energy compared to the full operation state of data centers in practical operating conditions of data centers.
The proposed method allows the orderly and systematic acquisition, under emergency conditions, of data that are useful for the management of a particular situation and for defining an initial cognitive state of the problem.
The standardization of the methodology made it possible to define the samples evenly, recreating the same conditions of data acquisition for all the subjects.
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