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In Kazakhstan, agricultural land has been degraded for decades, leading to an overall decline in crop yields. The purpose of the study is to conduct an environmental assessment of the current state of Kazakhstan’s soils to increase crop yields using soil protection methods. The following methods were used in the study: cartographic, analysis and synthesis, systematization, abstraction, specification, and forecasting. Agriculture accounts for 86% of Kazakhstan’s land. Active land use has led to various forms of degradation, resulting in 90 million hectares of eroded soils, of which 29.3 million hectares are almost completely eroded. In 2023, the average yield of grain crops was 9.3 c/ha, oilseeds – 7.1 c/ha, and vegetables – 275.4 c/ha. It is noted that the yield of grain crops in 2023 is 28% lower than in the previous period of 2020-2022, despite the increase in sown areas. Drought and soil degradation are the main reasons for the decline in grain yields. It is noted that the main biological constraints to grain production are soil drought, windstorms, degradation and extreme temperatures, especially in rainfed irrigated farming systems in arid lands such as southern Kazakhstan. In the northern and central parts of Kazakhstan, agricultural land is dominated by black and chestnut soils, and in the south by brown semidesert soils. The largest areas of eroded agricultural land are located in Almaty, Atyrau and Turkestan regions – over 30%, and the smallest – in Akmola, Karaganda, Kostanay and North Kazakhstan regions – 5%. Productive grain growing requires soil monitoring for the main mobile elements that affect plant yields. The average values for the Republic of Kazakhstan are: nitrogen content – 37.5 mg/kg, phosphorus – 16.8 mg/kg, potassium – 419.5 mg/kg, humus – 2.8%. The practical significance of the study is to expand and supplement the theoretical basis of research in the field of soil degradation, and the results obtained are proposed for a comprehensive environmental assessment of soils in Kazakhstan

This paper presents is to develop and compare neural network and conventional based controllers for a boiler of steam power plant. Designs of two different controllers for pressure and temperature are presented for keeping the boiler working in normal condition and improve efficiency. These controllers consist of NARMA controller of ANN and a conventional proportional-integrator-derivative (PID) controller. These parameters are adjusted by built a model and implementation in MATLAB program according to the requisite of the steam power plant and the control objectives. The results show a neural network is best controlled and superior performances of power plant from PID controller artificial neural network and PID have been applied in Al–Dura power plant in Baghdad. Therefore, neural networks have been extensively utilized in many industrial applications.

Leading role of trainers lies not only in presentation of training material, but mostly in organisation of training environment and development of managing influences, directed at activation of cognitive activity of students, which would have ensured learning necessary knowledge during a minimal time interval or a maximal volume of knowledge during a set time period.Ведущая роль преподавателей заключается не столько в изложении учебного материала, сколько в организации учебной среды и выработке управляющих воздействий, направленных на активацию познавательной деятельности учащихся, которая бы обеспечивала усвоение необходимых знаний за минимальное время, или максимального объема знаний за заданное время.