To solve this problem data have been converted to unit of the pollutants into g/l using the Eqs.
To solve this problem, the data set captured by our system was evenly separated into 20 sub-data sets along the slow axis.
To solve this, Scalia's data exchange platform uses machine learning to make product mapping simple, in addition to enriching existing data using computer vision for up to 17 data points.
To solve this problem, the data from the nearest stations to the centroid (longitude/latitude) of these LGAs were used to represent the meteorological data for these LGAs.
To solve this, 3D composition data with atomic resolution should be collected.
To solve this problem, the data are usually compressed before transmitting to a remote maintenance center.
To solve this limit, experimental data are extrapolated by a symmetrical polynomial fitting and imposing a zero integral to the stress profile.
Our results shed light on the role of sik1 in cardiomyogenesis and, although further investigation is required to solve this issue, our data provide a new piece of puzzle in the understanding of the molecular mechanisms regulating cardiomyocyte cell cycle, which is of vital importance for future clinical approach.
By solving this second data set, our GATSHM attains the same results obtained by some approaches such as in the MK01 for LEGA, MATSLO and TS3; in the MK02 for MATSPSO; in the MK03 for TS3, BBO and Heuristic; in the MK04 for BBO; in the MK05 for TS3 and BBO; in the MK07 for BBO and TS3; and in the MK08 for all the algorithms only it is not the case for the Heuristic.
In addition, by solving this first data set, our GATSHM attains the same results obtained by the chosen approaches such as in the case 1 for LEGA, BBO, Hybrid NSGA-II, and Heuristic; in the case 2 for MOPSOLS and BBO; in the case 3 for LEGA; in the case 4 for all the algorithms; and in the case 5 for MOPSOLS and Hybrid NSGA-II.
