We propose a method of the modeling and analysis of ionospheric parameters by combining wave let transform with autoregression models (integrated moving average). The method makes it possible to reveal regularities in ionospheric parameters and to make forecasts on variations. Also, this method can be used to fill the gaps in ionospheric parameters, with consideration of their diurnal and seasonal variations. The method was tested on foF2 data and data on the total electron content for the regions of Kamchatka and Magadan. The models constructed for the natural variation in ionospheric parameters allowed us to analyze its dynamical mode and build a forecast with a step of up to five hours. Based on estimates for model errors, we revealed anomalies arising during periods of increased solar activity and strong earthquakes in Kamchatka.

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The elemental composition of 100 oxalate type (only whewellite, weddellite and their mixture) urinary stones was determined by ICP OES. The calcium content varies from 130 to 320 g/kg; phosphorus – from 1.6 to 28 g/kg; sodium – from 0.54 to 8.7 g/kg; Ba, Fe, K, Li, Mg, Sr and Zn – from 0.0002 to 2.0 g/kg. The correlation analysis (Spearman rank correlation) showed a very weak tightness of the relationship between the elements of Ca/Li; a weak bond tightness for the pairs of elements Ba/Ca, Ba/Mg, Ba/Sr; Ca/Fe, Ca/Na, Ca/Sr; Fe/Mg, Fe/P, Fe/Sr, Fe/Zn; Li/Mg; Mg/Sr, Mg/Zn; P/Zn; average bond tightness for pairs of elements Ca/Mg, K/Na, Mg/P. All these correlations are positive.

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