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Direct coupling analysis or DCA is an umbrella term comprising several methods for analyzing sequence data in computational biology. [1] The common idea of these methods is to use statistical modeling to quantify the strength of the direct relationship between two positions of a biological sequence , excluding effects from other positions.
In Fixed Channel Allocation or Fixed Channel Assignment (FCA) each cell is given a predetermined set of frequency channels. FCA requires manual frequency planning, which is an arduous task in time-division multiple access (TDMA) and frequency-division multiple access (FDMA) based systems since such systems are highly sensitive to co-channel interference from nearby cells that are reusing the ...
The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4]
The sample size is relatively large (say, n > 10— ¯ and R charts are typically used for smaller sample sizes) The sample size is variable; Computers can be used to ease the burden of calculation; The "chart" actually consists of a pair of charts: One to monitor the process standard deviation and another to monitor the process mean, as is ...
Like approximate entropy (ApEn), Sample entropy (SampEn) is a measure of complexity. [1] But it does not include self-similar patterns as ApEn does. For a given embedding dimension, tolerance and number of data points, SampEn is the negative natural logarithm of the probability that if two sets of simultaneous data points of length have distance < then two sets of simultaneous data points of ...
If data is a Series, then data['a'] returns all values with the index value of a. However, if data is a DataFrame, then data['a'] returns all values in the column(s) named a. To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index.
The basic idea of the algorithm is this: a depth-first search (DFS) begins from an arbitrary start node (and subsequent depth-first searches are conducted on any nodes that have not yet been found). As usual with depth-first search, the search visits every node of the graph exactly once, refusing to revisit any node that has already been visited.
Additional data can be stored if edges are also stored as objects, in which case each vertex stores its incident edges and each edge stores its incident vertices. Adjacency matrix [3] A two-dimensional matrix, in which the rows represent source vertices and columns represent destination vertices. Data on edges and vertices must be stored ...