An important question is then, "Is the interval-based abstraction associated with a distributed computation consistent?" To answer this question, this paper introduces a consistency criterion named interval consistency (IC).
It is simple in concept, efficient in computation, consistent in performance, and reliable in signal processing.
The fractal dimension relation formulae are applied to China's cities and the cities of the United Kingdom, and the computations are consistent with the theoretical expectation.
In line with the experiments, our computations are consistent with the proximity (<7 Å) of V1814.58 to its symmetrical counterpart, during a 1 ns unrestrained all-atom simulation of the predicted lowest-energy configuration of the 4 dimer in an explicit POPC/10% cholesterol/solvent environment.
Intuitively, this criterion states that an interval-based abstraction of a distributed computation is consistent if its I-precedence relation does not contradict the sequentiality of each process.
A numerical example is used to illustrate that the results of the numerical computation are consistent with the theoretical conclusions.
A numerical example has been used to validate that the results of the numerical computation are consistent with the theoretical conclusions.
Computed heterogeneity indices are generated and compared to historical results, in order to check if the computation remains consistent after software update.
Although there are other strategies for imputing missing values, this computation is consistent with the previous PedsQL™ peer-reviewed publications, as well as other well-established HRQOL measures [ 37, 40, 41].
The trends in Table 3 and other results not shown indicate that, while increases in dataset size, relatedness/effective population size, and SNP density each increase computation time, consistent trends across all datasets and scenarios do not exist.
