Examples of using Each algorithm in English and their translations into Portuguese
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Each algorithm has deep optimization.
Graphical real-time display illustrating the frequency stages of each algorithm.
Each algorithm possesses the following properties.
A hash key is generated for each algorithm used, based on the database password.
We conclude, therefore,that this choice is independent on the number of possible locations of each algorithm.
We proved the linear convergence of algorithms  andsome numerical examples comparing two variants for each algorithm proposed were presented.
In this work, it is suggested the adequacy of each algorithm to databases with different densities and sizes.
Each algorithm was presented with orientation manuals, and the main clinical relevance of the different findings have also been addressed.
It supports most modes and variants of each algorithm, for compatibility with other systems.
Two ordering are considered, as a function of the weights of the vertices or the weights of the neighborhoods of the vertices.this leads to two versions of each algorithm.
Finally, a group of experiments is performed for each algorithms/procedures investigated in this research.
The sensibility of each algorithm for operating over-excitation limiter(oels) and on-load tap changer(oltcs) is evaluated.
The experiments were run in a PC Intel Core i7 3.40 GHz with 16 GB RAM, using a single core, with a time limit of 600 seconds andrunning five times each algorithm.
The agreement rates for each algorithm did not increase with a decrease in precision, i.e. with fewer possible APs located by the algorithm. .
For the results presented in Table 4, we observed no significant differences regarding sex and location of septal, right, andleft APs for each algorithm p> 0.05.
As each algorithm has its own parameters, the process to find their optimum values was done using an optimization algorithm,  nelder¿mead.
We used a ratio test to compare the accuracy rates for each algorithm, taking into account the obtained number of anatomical locations and number of observations.
Each algorithm has been newly designed specifically for the RV-500, providing incredibly unique effects while enhancing the popular modulations like chorus, phase, flange, vibrato, and tremolo.
We also evaluated the agreement rate between the observers by comparing the results of the assessment of each algorithm by the members of the research team in the 10 random ECGs.
This difficulty is intensified as each algorithm has a set of parameters that need to be adjusted to each  new situation, for its proper functioning.
Considering that the algorithms  identify a different number of anatomical regions around the AV annulus,the hypothesis to predict the AP location successfully accuracy is proportional to the number of regions for each algorithm.
Maybe fully in-depth knowledge of each algorithm is not needed to operate it in a test environment, but we do need to know what parameters can be manipulated and to what effect.
Additionally, an example of application of each  method to two radial distribution systems(distributionnetworks of 33 and 69 nodes) is shown to compare the results obtained in terms of the efficiency and effectiveness of each algorithm.
From these simulations, six performance indexes are used for the quantitative evaluation of each algorithm, from where the most effective method can be determined through the arithmetic mean of these indexes. lastly, after all the.
But, to each algorithm, there may or may not correspond a real number, as the algorithm  may fail to satisfy the constraints, or even be non-terminating("T" is a partial function), so this fails to produce the required bijection.
The structure of neighborhoods, as well as the parameters of the algorithms,  were widely tested and analyzed,being possible verify how the parameters affect the behavior of each algorithm implemented and search the best parameters.
Regardless of the number of locations that each algorithm is able to identify, it was possible to highlight a single algorithm  for each  location septal, right and left as the most suitable to approximately estimate the location of the APs.
Participatory search is an algorithm  in which search progresses forming pools of compatible individuals, keeping the one that is the most compatible with the current best individual in the current population, andintroducing random individuals in each algorithm step.
The agreement rates obtained for each algorithm approached the results of other similar studies, which allows us to conclude that all analyzed algorithms have lower agreement rates than those published by their authors.