INDEX evaluation

Vertzoni et al. (30) recently clarified the applicability of the similarity factor, the difference factor, and the Rescigno index in the comparison of cumulative data sets. Although all these indices should be used with caution (because inclusion of too many data points in the plateau region will lead to the outcome that the profiles are more similar and because the cutoff time per percentage dissolved is empirically chosen and not based on theory), all can be useful for comparing two cumulative data sets. When the measurement error is low, i.e., the data have low variability, mean profiles can be used and any one of these indices could be used. Selection depends on the nature of the difference one wishes to estimate and the existence of a reference data set. When data are more variable, index evaluation must be done on a confidence interval basis and selection of the appropriate index, depends on the number of the replications per data set in addition to the type of difference one wishes to estimate. When a large number of replications per data set are available (e.g., 12), construction of nonparametric or bootstrap confidence intervals of the similarity factor appears to be the most reliable of the three methods, provided that the plateau level is 100. With a restricted number of replications per data set (e.g., three), any of the three indices can be used, provided either non-parametric or bootstrap confidence intervals are determined (30). 

The second difference relates to the definition of a cutoff time point for the evaluation of the difference factor and the Rescigno index. When cumulative data are available, evaluation of the difference factor or the Rescigno index usually requires a reference data set in order to define the cutoff time point for index evaluation (30). For the evaluation of fl and the , i.e., when the difference factor and the Rescigno index are evaluated from non-cumulative data, this difficulty does not exist, provided that the release process has been monitored up to the end (i.e., until dissolution of the drug is complete). At this point, it is worth mentioning that a similar conclusion cannot be drawn for the similarity factor (31) because application of this index to non-cumulative data is set apart by the careful scaling procedure required, in addition to the existence of a reference data set. The reason is that this index can continue to change even after dissolution of both products is complete. 

ABSTRACT By analyzing the hierarchy of safety factors in purification plant of natural gas, they are divided into personnel, equipment, environment and management. After the study of safety index, evaluation methods and fuzzy arithmetic method for each hierarchy, its fuzzy evaluation flow is given. During the evaluation, the weight of the factors and each hierarchy is decided by analytical hierarchical process, and the operational criterion adapts maximum membership degree. And this model is exemplified in purification plant of natural gas. The results show that the second fuzzy evaluation is effective to assess purification plant of natural gas. 

Then put the index of early-warning object into the set of various levels to do multi-index evaluation ... 

Thus, the /nth element of the matrix, Z)), , is the approximate value of the differential operator, L, of the nth basis function (where n is the column index) evaluated at the /th collocation point... 

Three ways are used including susceptibility evaluation, risk index evaluation and the risk degree of debris flow. 

Linear density calculated as a total length of lineaments within territorial entity.. K -index evaluated as a sum of lineament intersection within territorial entity. Blockiness determined as an average square of blocks bounded lineaments and situated in one territorial cell. 

Sofi F, Cesari F, Abbate R, Gensini GF, Casini A. Adherence to Mediterranean diet and health status meta-analysis. BMJ. 2008 337 al344. Bach A, Serra-Majem L, Carrasco JL, et al. The use of indexes evaluating the adherence to the Mediterranean diet in epidemiological studies a review. Public Health Nutr. 2006 9(1A) 132—146. 

The inhomogeneity index evaluated at the mean cluster scale is a relative measure for the inhomogeneity in the binary pictures. It can be used to describe the increase or decrease of inhomogeneity of objects. An increase of the... 

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