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Predictions based on the cumulative curves: Basic principles and nontrivial example to the global warming (GW) phenomenon. These data are considered as nontrivial examples of verification of new

Predictions based on the cumulative curves: Basic principles and nontrivial example to the global warming (GW) phenomenon. These data are considered as nontrivial examples of verification of new

Application of the generalized mean value function to the statistical detection of water in decane by near-infrared spectroscopyThe generalized mean value (GMV) function, defined as GN(p)=(ΔN(p))1/ p(where ΔN(p) is the absolute

Application of the generalized mean value function to the statistical detection of water in decane by near-infrared spectroscopyThe generalized mean value (GMV) function, defined as GN(p)=(ΔN(p))1/ p(where ΔN(p) is the absolute

The statistics of the fractional moments: Is there any chance to "read quantitatively" any randomness? analyzed to a finite set of k statistically stable parameters, keeping invariant the values of the first

Analysis of a nanofilm of the mercaptophenyl diazonium modified gold electrode within new statistical parameters of statistical and robust quantitative values. Besides, there is a possibility to receive the relative

New method of "reading" strongly-correlated sequences: Treatment and analysis of the CCD-matrix noise - distribution. Relative fitting error does not exceed the value of 2.2% with the value of Pearson correlation

Application of fractional-moments statistics to data for two-phase dielectric mixtures is suggested. This method is based on ideas related to the application of the generalized mean value (GMV

The generalized mean value function approach: A new stastistical tool for the detection of weak signals in spectroscopy of moments and is based upon the definition of the correct fit to the generalized mean value (GMV) function

The statistics of the fractional moments: Is there any chance to "read quantitatively" any randomness? analyzed to a finite set of k statistically stable parameters, keeping invariant the values of the first

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