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The results showed that ANN can be an alternative approach for the predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives.
The process of predicting the core promoter can therefore be summarized as using these characteristics to locate the TSS.
Using this principle for the present study, we first focused on predicting the core TRRs and then illustrated how these core parts are common for different transcripts and conserved across species.
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Pundits predicted the core of a government would be a coalition between the two winners, the VVD led by Mark Rutte and Labour under the new leadership of Job Cohen, mayor of Amsterdam until earlier this year.
MARS-LMR well predicts the core flow and power change by reactivity feedbacks in the core.
Experimental results were also obtained by building models according to artificial neural network (ANN) to predict the core compressive strength.
These data were used in the CWHP system to compute and predict the core body temperature of the virtual construction worker.
In this study, an artificial neural networks study was carried out to predict the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives.
The CBTP system predicts the core body temperature of each construction worker, based on the estimated thermal environment data obtained by the TEP system, the worker's physiological data, and planned work site location information.
The concept of machine learning, with its CI paradigm, has been instrumental to the use of existing data such as well logs and their accompanying core measurements to predict the core values for new wells and uncored sections of wells or new fields.
We applied the CBTP system to a hypothetical construction site at Osaka University, using the VRATE system and found that this system could predict the core body temperature, which varies throughout the day, and predict the risk of heatstroke according to core body temperature for each time zone.
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