Algorithms checks

The program can use conventional, in-core, or direct integral evaluation. The default ah initio algorithm checks the disk space and memory available. It then uses an in-core method if sufficient memory is available. If memory is not available for in core evaluation, the program uses a conventional method if... 

Similarly to the concept of the Catalyst software package [35, 40], hydrophobic areas are implemented in the form of spheres located in the center of hydro-phobic atom chains, branches or groups. First, a hydrophobicity scoring function pursuant to the Catalyst definition is implemented. As a next step, the algorithm checks if an ensemble of adjacent atoms is able to attain a sufficient overall hydrophobicity score. If this condition is met and a hydrophobic area in the macromolecule exists, a level 4 feature consisting of a sphere with a tolerance... 

The algorithm checked every investigated reaction for a minimum number of needed reactions and reactants needed to produce the products. A greedy approach was employed for minimizing the number of reactants and reactions needed to produce the products. 

The reverse search refers to the fact that the algorithm checks whether a peak from the reference spectrum is present in the unknown (and in the appropriate abundance) and not the other way around. In this way, the reverse search ignores peaks in the unknown that are not present in the reference. The mass library searches are, in fact, much more elaborate, and provide at the end a list of possible matches and for each of them a calculated percentage match. 

Modem software has built-in UNIFAC automatic decomposition algorithms. Check the availability of the UNIFAC parameters when the molecule decomposition is not obvious, or it contains less common atoms. Note also that there are several updates of UNIFAC parameters. 

These systems have shown themselves to be capable of identifying raw materials which are variable enough to cause downstream processing problems even though the more sensitive but more specific traditional types of analyses have found the materials to be acceptable. Often this approach is taken further and a prediction model developed whereby a qualitative model, using conformity or correlation algorithms checks the identity (ID) then a quantitative model uses the same data to predict a value of actual physical parameters such as moisture, concentration, and even particle size [4]. 

In the special case of a knovm interval of uncertainty, there is a test that provides a maximum guarantee of reliability. If the algorithm checks the interval of uncertainty [Ia, h] and the best point between (a and ts is selected as the solution, the error on the solution is... 

Once the algorithm is initialized, it starts with the first input example, and determines the fuzzy subspace that is closer to that data point. The center of this subspace becomes the center of the first hidden node. Each time a new input example is available, the algorithm checks whether it can be assigned to an already selected fuzzy subspace. If this cannot be done then a new node must be added to the hidden layer. In this case, the center of the new hidden node is located at the center of the fuzzy subspace which is closer to the new input vector. Otherwise, the algorithm checks whether there is any center which has not been assigned recently to an input vector (information about the history of node activations is stored by the algorithm in a vector called Activation History Vector - AHV). If such a center exists, this center is removed. 

During data entry several activities take place. These include check of plausibility start of algorithms check of limiting values change of status quality assurance and costing. 

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