In laboratory tests, the effectiveness of water plant extracts obtained from 20 species of herbal plants on the degree of contamination of white mustard seeds (Sinapis alba L.) by fungal and bacterial pathogens was evaluated. The analysis showed that the contamination of the tested seed material was statistically significantly influenced by the plant species from which the biopreparations were obtained. However, there were no significant differences in the method of preparation of extracts for the examined traits. The tested natural preparations had a different effect on limiting the superficial growth of bacteria and fungi on seeds. However, infusion and macerate obtained from the bark of Salix alba L. were the most effective in limiting the contamination with both bacterial and fungal pathogens. The number of seeds infected with fungi after the application of these preparations was 24.25% on average, whereas in the case of bacterial colonization of the tested material, the value of this indicator was 1.00% on average.

Turning time occupies a significant part of the operations carried out by implement-and-tractor aggregates, especially in fields with short runs. Incorrectly executed turns increase the width of the turning strips, significantly increasing the idle path of the implement-and-tractor aggregate, with negative effect on its efficiency. The objective of this paper was to theoretically analyse the turning agility of an asymmetric implement-and-tractor aggregate, taking into account its forward speed and design parameters. Considering a trailed asymmetric swath reaper and tractor aggregate, the obtained equations allowed a numerical simulation in order to evaluate the headland turning agility of this implement-and-tractor aggregate. The minimal radii of the trailed asymmetric swath reaper and tractor aggregate are, respectively, 8.33 m for right-side turn and 4.90 m for left-side turn. Furthermore, the optimal angle between the longitudinal axis of the aggregating tractor and the hitch bar of the trailed asymmetric implement exists only in the case of left-side U-turns and its value is 1.12 rad (64°). It is not possible to cover right-side U-turns or both right- and left-side pear-shaped loop-turn in the optimal mode.

In the context of increasing demands for the reliability and efficiency of electrical complexes and systems, the problem of assessing and monitoring the technical condition (TC) of electrical equipment is becoming particularly relevant. This review is devoted to a comprehensive analysis of the factors affecting the performance of electrical equipment and modern methods for diagnosing its TC. The review article examines in detail the impact of various operational factors, including climatic conditions (temperature fluctuations, humidity, contamination) and electrical equipment operating modes. Special attention is paid to modern methods of technical diagnostics, such as thermographic diagnostics, vibration diagnostics, and chromatographic analysis of dissolved gases, which make it possible to identify defects and predict failures at early stages of their development. A significant part of the review is devoted to modern approaches to predicting the durability indicators of electrical equipment using mathematical modeling and neural networks. The advantages of a condition-based maintenance (CBM) and repair strategy, based on assessing the actual TC of the equipment, are analyzed in detail and compared with the strategy of scheduled preventive maintenance. This review particularly emphasizes the importance of integrating digital technologies, including the internet of things (IoT), digital twins (DT), and intelligent diagnostic monitoring systems, to create effective systems for predicting and managing TC. The review demonstrates that a comprehensive consideration of the actual TC of electrical equipment and its operating conditions can significantly increase the reliability of power systems, optimize maintenance and repair costs, and extend the service life of electrical equipment under various intensities of impacting operational factors.

Forecasting electricity consumption is one of the most important scientific and practical tasks in the field of electric power engineering. The forecast accuracy directly impacts the operational efficiency of the entire power system and the performance of electricity markets. This paper proposes algorithms for source data preprocessing and tuning XGBoost models to obtain the most accurate forecast profiles. The initial data included hourly electricity consumption volumes and meteorological conditions in the power system of the Republic of Tatarstan for the period from 2013 to 2025. The novelty of the study lies in defining and justifying the optimal model training period and developing a new evaluation metric for assessing model efficiency—financial losses in Balancing Energy Market operations. It was shown that the optimal depth of the training dataset is 10 years. It was also demonstrated that the use of traditional metrics (MAE, MAPE, MSE, etc.) as loss functions during training does not always yield the most effective model for market conditions. The MAPE, MAE, and financial loss values for the most accurate model, evaluated on validation data from the first 5 months of 2025, were 1.411%, 38.487 MWh, and 16,726,062 RUR, respectively. Meanwhile, the metrics for the most commercially effective model were 1.464%, 39.912 MWh, and 15,961,596 RUR, respectively.

The article presents research results of a machine-tractor unit that performs two technological operations simultaneously: (i) chopping plant residues (sunflower stubble); (ii) covering the chopped stubble with the soil. The first operation is carried out with a front-mounted plant residues chopper, and the second one is carried out with a rear-mounted plough. The chopper’s working devices are rotated by the tractor’s front power take-off (PTO), which has two operating modes: 540 and 1000 rpm. It was determined that to reduce the dynamic load in the drive of the chopper’s plant residues working devices, to chop these residues qualitatively, and then to cover them with the soil, the tractor’s front PTO should be adjusted to a speed of 1000 rpm. With this mode of the chopper’s working device’s rotation, the difference in its vertical vibrations’ dispersion and the tractor front axle’s oscillations is insignificant. The variance of the plowing depth vibrations (1.44 cm²), changing aperiodically in the frequency range of 0–2.5 Hz, is not accidentally less than the variance of irregularities vibrations of the longitudinal field profile (2.75 cm²). The plough draft resistant oscillations of the plow-chopping unit had the least impaction at the plowing depth oscillations. The proof of this is the small value of the cross correlation function; for such oscillating processes as ‘plough draft resistance—plowing depth’, it was equal to 0.22, which is 3.4 times less than for oscillating processes ‘surface’s longitudinal profile—plowing depth’. The number of chopped particles less than 15 cm in length increased by 1.5 times, and the number of particles longer than 30 cm decreased by 3 times. With the complete incorporation of plant residues into the soil, their non-chopped part did not exceed 1%.

One of the most important parameters that characterize the traction-coupling properties of a wheeled tractor is its slip. The more tractor’s gross traction exists, the higher its traction-coupling properties are. However, this gross traction should not exceed its maximum possible value, which, in turn out, is to be determined by the maximum permissible slip, ${\delta }_{max}$. This article provides the equation to calculate this crucial parameter and establishes the dependencies between the tractor’s slip and soil structure coefficient. It was shown that the value of ${\delta }_{max}$ basically depends on such soil characteristics as the bulk deformation coefficient and the coefficient of rolling resistance. Calculations showed that, for the average value of the soil bulk deformation coefficient at $4000 \mathrm{kN}·{\mathrm{m}}^{-3}$, the average value of rolling resistance coefficient at 0.16, and the ratio value of the maximum permissible soil pressure to the tractor wheel rolling radius at $222 \mathrm{kPa}·{\mathrm{m}}^{-1}$, the maximum permitted amount slip of the tractor wheels should not exceed 15%. With more slip, the soil structure deteriorates significantly. In this case, its structure coefficient may be less than critical, equal to 0.4.