The lich‑ root as well as its marginal semantic lines are described here. The description is based upon folk and mythological images and also upon etymologic data, including the meaning of this root and its derivatives in Slavonic languages (chiefly in Russian and Ruthenian). The semantics is labelled as excess (superfluity), insufficiency (lack), harmfulness (damage), dash (Dutch courage) as well as being presented with the semantic signs and forms reflected in words. Presented is the possible semantic mechanism for the development and transition from the initial meanings to newer ones. We explain the logic of the association root in question with the old images of lot and moderation. Future research prospects for the lich‑ root as well as its semantic and morphological derivation are shown. This includes not only literary variants but equally dialectal and regional ones.

The article analyses the development of metrological control technologies for electronic distance measurement rangefinders to determine their main characteristic of accuracy – the root mean square error of distance measurement. It is established that the current reference linear bases are reliable and serve as the main means of transmitting a unit of length from the standards to the working means of measuring length. The article describes the existing linear reference bases and specifies their accuracy and disadvantages. It is concluded that the disadvantages of linear reference bases are deprived of the reference linear bases built in special laboratories. They use distances measured by the differential method with laser interferometers as reference distances. The application of such technology allowed to automate the processes of measurements and calculations. There is development of fibre-optic linear bases, in which optical fibres of known length are used as model lines. The article offers a new technical solution – a combination of fiber-optic and interference linear bases, which allows to qualitatively improve the system of metrological support of laser rangefinders. This is achieved by having a fiber-optic unit, which allows you to create baselines of increased length, while ensuring small dimensions of the baseline, and relative interference base, which provides high accuracy of linear measurements and does not require calibration of the base with a precision rangefinder, which eliminates several difficulties associated with changes in the refractive index, makes measurements independent of the wavelength of the radiation source and almost independent of the ambient temperature.

Artificial neural network models (ANNs) were used in this study to predict reference evapotranspiration ( ET_o) using climatic data from the meteorological station at the test station in Kafr El-Sheikh Governorate as inputs and reference evaporation values computed using the Penman–Monteith (PM) equation. These datasets were used to train and test seven different ANN models that included different combinations of the five diurnal meteorological variables used in this study, namely, maximum and minimum air temperature ( T_max and T_min), dew point temperature ( T_dw), wind speed ( u), and precipitation (P), how well artificial neural networks could predict ET_o values. A feed- forward multi-layer artificial neural network was used as the optimization algorithm. Using the tansig transfer function, the final architected has a 6-5-1 structure with 6 neurons in the input layer, 5 neurons in the hidden layer, and 1 neuron in the output layer that corresponds to the reference evapotranspiration. The root mean square error ( RMSE) of 0.1295 mm∙day ^–1 and the correlation coefficient ( r) of 0.996 are estimated by artificial neural network ET_o models. When fewer inputs are used, ET_o values are affected. When three separate variables were employed, the RMSE test values were 0.379 and 0.411 mm∙day ^–1 and r values of 0.971 and 0.966, respectively, and when two input variables were used, the RMSE test was 0.595 mm∙day ^–1 and the r of 0.927. The study found that including the time indicator as an input to all groups increases the prediction of ET_o values significantly, and that including the rain factor has no effect on network performance. Then, using the Penman–Monteith method to estimate the missing variables by using the ET_o calculator the normalised root mean squared error ( NRMSE) reached about 30% to predict ET_o if all data except temperature is calculated, while the NRMSE reached about of 13.6% when used ANN to predict ET_o using variables of temperature only.