Compilers

Compilation of physical properties for 321 heavy hydrocarbons. Vapor pressures at low pressures. ... 

Correlation and compilation of vapor-pressure data for pure fluids. Normal and low pressure region. 

Compilation of azeotropic data as well as other physical properties including melting and boiling points. 

Compilation of binary experimental data reduced with the Wilson equation and, for high pressures, with a modified Redlich-Kwong equation. 

Compilation of vapor-pressure data for organic compounds data are correlated with the Antoine equation and graphs are presented. 

Extensive compilation of vapor-liquid equilibrium data, particularly from Eastern Europe. 

Compilation of vapor-liquid equilibrium data data are correlated with Redlich-Kister equation (in Polish). 

Compilation of pure-component data including vapor pre s sure s. 

Compilation of data for binary mixtures reports some vapor-liquid equilibrium data as well as other properties such as density and viscosity. 

Thorough compilation of literature sources for binary and multicomponent data includes many references to the East European literature. ... 

Compilation of vapor pressures of organic and related compounds to one atmosphere. 

Values of dipole moment y are listed in the compilation by McClellan (1974). 

The computer subroutines for calculation of vapor-phase and liquid-phase fugacity (activity) coefficients, reference fugac-ities, and molar enthalpies, as well as vapor-liquid and liquid-liquid equilibrium ratios, are described and listed in this Appendix. These are source routines written in American National Standard FORTRAN (FORTRAN IV), ANSI X3.9-1978, and, as such, should be compatible with most computer systems with FORTRAN IV compilers. Approximate storage requirements and CDC 6400 execution times for these subroutines are given in Appendix J. 

The program storage requirements will depend somewhat on the computer and FORTRAN compiler involved. The execution times can be corrected approximately to those for other computer systems by use of factors based upon bench-mark programs representative of floating point manipulations. For example, execution times on a CDC 6600 would be less by a factor of roughly 4 than those given in the tcible and on a CDC 7600 less by a factor of roughly 24. 

The list of contributors is on page 6 and I am deeply indebted to them, for it is they who originally prepared MiaU s Dictionary of Chemistry from which this Dictionary has been compiled. Errors and omissions are my responsibility and 1 would appreciate receiving notice of them. 

The DIPPR Pure Component Data Compilation, Numerica (TM) Version 9.2 (1994). 

Sampled scan data would be a benefit when it comes to certification of new inspectors or re-certification of existing staff. A large database of scans could be compiled and used randomly at test centres, which would help to avoid the samples becoming too familiar. Examinations could also be more easily tailored to the probes, types of materials and types of defects the inspector is likely to see. 

Calculated from various theoretical equations some of the sources are themselves compilations rather than original. 

Although the Femii contact mechanism dominates most couplings, there are smaller contributions where a nuclear dipole physically distorts an orbital, not necessarily of s type [18]. There are many useful compilations of J and K values, especially for FIFI couplings (see [9], eh 4, 7-21 and [12, 13,14 and 15]). 

As source of infonnation we use the Surface Structure Database [14], a critical compilation of surface structures solved in detail, covering the period to the end of 1997. It contains 1113 structural detenninations with, on average, two detenninations for each stmcture thus there are approximately 550 distinct solved stnictures available. 

Nomenclature is the compilation of descriptions of things and technical terms in a special field of knowledge, the vocabulary ofa technical language. In the history of chemistry, a systematic nomenclature became significant only rather late. In the early times of alchemy, the properties of the substance or its appearance played a major role in giving a compound a name. Libavius was the first person who tried to fix some kind of nomenclature in Alckeinia in 1,597. In essence, he gave names to chemical equipment and processes (methods, names that are often still valid in our times. 

Next, an attempt was made to evaluate the quantitative importance of the various reaction schemes [19]. To this effect, a printed compilation of 1900 reactions dealing with the introduction of one carbon atom bearing a functional group [20] was analyzed and each reaction assigned manually to a corresponding reaction scheme. The results are Hsted in Table 3-3. 

Clearly, this choice of a reference set of organic reactions is arbitrary, not necessarily representative of the whole set of organic reaction types described in the literature, and therefore not free from bias. However, it does give some indication of the relative importance of the various reaction schemes. It is quite clear that the reaction scheme shown in Figure 3-13 (R1 of Table 3-3) comprises the majority of organic reactions in most compilations of reactions it will account for more than 50 % of all reactions. 

Once we have defined this, we have to compile the initial dataset. First of aU, we decide upon the composition of the dataset. Usually, we take initially as many compounds as possible. 

The final stage of compiling a maximally refined dataset is to split it into a training and test dataset. The definition of a test dataset is an absolute must during learning, as, in fact, it is the best way to validate the results of that learning. 

Although the problem of compilation of training and test datasets is crucial, unfortunately no de-faao standard technique has been introduced. Nevertheless, we discuss here a method that was designed within our group, and that is used quite successfully in our studies. The method is mainly addressed to the task of finding and removing redundancy. 

The easiest way to extract a set of objects from the basic dataset, in order to compile a test set, is to do so randomly. This means that one selects a certain number of compounds from the initial (primary) dataset without considering the nature of these compounds. As mentioned above, this approach can lead to errors. 

Another method of detection of overfitting/overtraining is cross-validation. Here, test sets are compiled at run-time, i.e., some predefined number, n, of the compounds is removed, the rest are used to build a model, and the objects that have been removed serve as a test set. Usually, the procedure is repeated several times. The number of iterations, m, is also predefined. The most popular values set for n and m are, respectively, 1 and N, where N is the number of the objects in the primary dataset. This is called one-leave-out cross-validation. 

Hence, the main danger in the process of compiling test sets remains. Fortunately, there are some other approaches which, although they may look more sophisticated, do diminish the possibiHty of such incompatibilities. 

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