Changes in river channel morphological parameters are influenced by anthropogenic factors, such as climatic changes, river catchment management changes, and hydrotechnical development of rivers. To assess the intensity of individual pressures and the resulting changes in abiotic and biotic factors in the riverbed, water quality monitoring is conducted, including the assessment of the hydromorphological status. The assessment can be based on the River Habitat Survey (RHS) which is a synthetic method that includes the evaluation of habitat character and river quality based on their morphological structure.
The input data, which characterise any river include physical features of hydrotechnical structures, bed granulation, occurrence of bedforms, visible morphodynamic phenomena, and a sediment transport pattern. The RHS method allows to determine two quantitative indices used to evaluate the hydromorphological status: Habitat Modification Score ( HMS), which determines the extent of transformation in the morphology of a watercourse, and Habitat Quality Assessment ( HQA), which is based on the presence and diversity of natural elements in a watercourse and river valley.
The proposed method can be divided into three stages. The first assesses the river section hydromorphological indices, describing the degree of technical modification ( HMS) and the ecological quality of the reach ( HQA), using the RHS method. The second stage describes morphological changes resulting from the technical regulation and estimates indices for the regulated reach. Finally, we compare HQA and HMS indices before and after the regulation. This comparison is described by numerical indicators and related to reference values.

The article is to present the application of genetic algorithm in production scheduling in a production company. In the research work the assumptions of the methodology were described and the operation of the proposed genetic algorithm was presented in details. Genetic algorithms are useful in complex large scale combinatorial optimisation tasks and in the engineering tasks with numerous limitations in the production engineering. Moreover, they are more reliable than the existing direct search algorithms. The research is focused on the effectivity improvement and on the methodology of scheduling of a manufacturing cell work. The genetic algorithm used in the work appeared to be robust and fast in finding accurate solutions. It was shown by experiment that using this method enables obtaining schedules suitable for a model. It