Chainsaw operation is associated with several hazards, including exposure to wood dust, a risk factor which requires serious attention due to health impacts such as respiratory disease. This study aims to investigate the effect of saw chain type and wood species on the mass concentration of airborne wood dust during chainsaw cross-cutting operations. Real-time inhalable and respirable dust measurements in the breathing zone of the chainsaw operator were carried out using a desktop aerosol monitor. Three types of cutter shape (semi chisel, full chisel, and chamfer chisel) and four types of wood species (oak, beech, spruce, and fir) were used in the experiments. The split-plot ANOVA results showed that both respirable and inhalable mass concentrations of wood dust were affected exclusively by wood species. No statistically significant differences were found among particular levels of cutter shape. The highest average mass concentrations of airborne dust were generated by the combination of oak wood and a full chisel cutter.

In order to keep the home and occupational environment clean and non-infectious, the consumption of cleaners and disinfectants, including cosmetics, is increasing. Excessive use of these products results in their accumulation in the aquatic environment. Conventional wastewater treatment plants are unable to effectively remove the emergent pollutants, including personal care products. This article is focused on the monitoring of the presence of personal care products in surface waters in two river basins in the Slovak Republic, in terms of the surfactant content. Ecotoxicological evaluation of the selected samples from the monitored river basins was performed by an acute toxicity test using the test organism Daphnia magna. The monitoring results indicate the presence of personal care products in the aquatic environment which poses an ecological and environmental risk. Monitoring in the Hron and Nitra river basins confirmed contamination with the surfactants, to which the measures related to the COVID-19 pandemic contributed. The content of the surfactants in personal care products is significant, and their impact on the aquatic environment is not sufficiently monitored.

In this article, the authors have experimentally investigated the changes in four key properties of six non-edible low-impact energy carries based on rapeseed oil quality grade HM and viscosity grade VG46, which were used as a filling in the hydraulic system of a round wood sorting and transporting trolley. These oils were enriched with thermo-oxidizing, extreme-pressure additives, anti-foaming, and lubricating additives to enhance performance. Three supervised machine learning prediction algorithms were used to predict key parameters essential for optimizing their performance and RUL (remaining useful life), namely support vector regression (SVR), generalized additive model (GAM), and Gaussian process regression (GPR). The model’s performance was scored from multiple perspectives using metrics such as root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R²) to state actual values, thereby demonstrating the validity of the models in predicting lubricant lifespan. Based on the collected data, this study demonstrated that it is possible to predict the degradation of hydraulic oil factors to the limit state, integrate these parameters into a comprehensive metric for more accurate remaining useful life (RUL) estimation, and obtain actual operating trends. A negative correlation was found between the remaining useful life (RUL) and parameters such as acid number, kinematic viscosity, peroxide number, and water content. The comparison of modeling algorithms showed that all three algorithms adequately described the degradation patterns. By using these performance criteria, we defined the most accurate and reliable soft-computing model for predicting hydraulic fluid parameters, providing valuable insights into optimizing machine learning models for practical applications.

This paper investigates the colour and mechanical properties of European beech (Fagus sylvatica L.) false heartwood to propose a framework for its enhanced utilisation in interior furniture components. The primary objective was to examine the relationship between wood hardness and CIE L*a*b* colour coordinates across different false heartwood zones. Regression analysis revealed a strong negative relationship between the b* colour coordinate and Janka hardness (R² = 0.716). Additional analyses of reflectance spectra coupled with scanning electron microscopy (SEM; Tescan Vega and UHR-SEM Tescan Amber) distinguished degraded from non-degraded zones based on their microstructural integrity. Microscopic analysis showed that early-stage colour changes occur without structural degradation or the presence of fungal hyphae, thereby preserving the microstructural integrity of the wood. Conversely, significant hardness loss was associated exclusively with advanced stages of biological degradation characterized by the evident presence of fungal hyphae within the wood microstructure. These findings suggest that optical parameters can serve as reliable, non-destructive indicators for the automated and objective grading of beech wood containing false heartwood, thereby optimizing its value and utilisation in the wood-processing industry.