Energy balance of the photovoltaic system is influenced by many factors. In this article the effect of tilt and azimuth angle changes of the photovoltaic system energy production is analyzed. These parameters have significant impact on the amount of solar radiation which hits on the photovoltaic panel surface and therefore also on the energy absorbed by the module surface. The main aim of research was identification of the optimal position of photovoltaic system installation in the southern Slovakia regions. The experimental apparatus had two setups consisting of polycrystalline photovoltaic modules. The first setup was used for identification of the tilt angle changes in the range (0–90°). The second one was focused on the detection of the azimuth angle effect to the energy production. The measurement results were statistically processed and mathematically analyzed. Obtained dependencies are presented as two-dimensional and three-dimensional graphical relations. Regression equations characterize time relations between the tilt or azimuth angle and the energy produced by the photovoltaic system in Southern Slovakia. Obtained simplified mathematical model was verified by analytical model. Presented models can be used for the dimensioning and optimization of the photovoltaic system energy production.

Worldwide, agricultural land is a dominant part of the environment. It is very important to understand the physical properties of soil because they directly or indirectly affect the entire human population. This paper proposes a data acquisition system for an original design of the soil resistance force sensor (SRFS). It serves to evaluate the properties of soil affected and unaffected by tractor passages through the field. The SRSF uses two cutting blades to measure soil mechanical resistance within the tire track and outside the tire track. The proposed system consists of two load cells, datalogger, power supply and software for personal computers. The system was practically tested under field operation. The results showed significant differences between the soil resistance force measured outside the tire track and within the tire track after one, two and three tractor passages. The data were compared with penetrometer resistance and soil bulk density, standardly characterizing soil mechanical resistance. An increase of soil resistance force after one, two and three tractor passages corresponded with an increase in reference parameters. The results showed that the proposed system is suitable for practical applications to evaluate soil mechanical resistance using SRFS.

The issue of navigation methods is being continuously developed globally. The aim of this article is to test the fuzzy control algorithm for track finding in mobile robotics. The concept of an autonomous mobile robot EN20 has been designed to test its behaviour. The odometry navigation method was used. The benefits of fuzzy control are in the evidence of mobile robot’s behaviour. These benefits are obtained when more physical variables on the base of more input variables are controlled at the same time. In our case, there are two input variables - heading angle and distance, and two output variables - the angular velocity of the left and right wheel. The autonomous mobile robot is moving with human logic.

The modeling of pan evaporation (Ep) trends in Slovak river sub-basins was conducted using advanced artificial intelligence (AI) techniques algorithms to accurately calculate evaporation rates based on daily climate data from 2010 to 2023 across eight sub-basins in the Slovak Republic. The AI modeling results reveal that the Bodrog, Hornád, Ipeľ, Morava, Slaná, and Váh river basins are experiencing increases in evaporation, while the Dunaj and Hron rivers show declining trends. This divergence may indicate varying ecological factors influencing the evaporation dynamics of each river. A comprehensive set of 28 machine learning (ML) and deep learning (DL) models was employed, including ML techniques such as linear regression, tree-based, support vector machines (both with and without kernels), ensemble, and Gaussian process methods; as well as DL approaches like neural networks (narrow, medium, wide, bilayered, and trilayered). Among these, stepwise linear regression provided the most optimal fit. The minimum redundancy maximum relevance (mRMR) method was utilized for feature selection to balance relevance and redundancy effectively. The results suggest that emphasizing relative humidity (RH) and minimum temperature (t_min) significantly enhances accuracy, highlighting the critical roles of these factors in modeling pan evaporation trends. The results offer precise evaporation analyses to improve water management and lessen scarcity.

Driving a vehicle with a passive trailer has been the subject of numerous studies. Current control strategies are mostly used by differential geometry, linear algebra, fuzzy regulators, and artificial neural networks. The objective of this study is to design an algorithm for autonomous control of the tractor-trailer system reversing based on an algorithm using a PSD controller and to verify it on a simulated mathematical model. All parameters listed in the models and experiments are performed based on the existing tractor-trailer system. Dynamic models of steering and velocity control were identified and incorporated into the simulation. Result includes a stable operation of the steering without oscillating. The proposed algorithm can be implemented in microcontrollers without the need for high computing power.