Mendel University in Brno

ABSTRACTIn this work, we developed biodegradable cellulose acetate (CA) fibers infiltrated with ZnO nanocrystals for the Acne vulgaris (AV) treatment. The antibacterial effect (through Zn2+ release) was activated upon oxygen plasma treatment. CA fibers (fiber diameter ≈ 2 µm) were produced via centrifugal spinning, followed by ZnO infiltration (up to 8 wt% of Zn content) using Vapor Phase Infiltration (VPI) with varying deposition cycles. As CA fibers are naturally hydrophobic, oxygen plasma treatment was applied to turn them hydrophilic and to improve skin wetting. The combination of these steps is presented for the first time. Plasma treatment introduced oxygen‐containing functional groups on the CA surface, significantly improving wettability. Scanning electron microscopy analyses of treated fibers showed no morphological damage. Detailed characterization (using X‐ray photoelectron spectroscopy, Raman spectroscopy, energy dispersive X‐ray fluorescence, X‐ray diffraction, tranmission electron microscopy) focused on fibers with 32 and 128 VPI cycles, revealed that plasma partially oxidized and etched the surface, affecting the ZnO distribution. Plasma‐modified fibers, unlike untreated ones, exhibited antibacterial activity against AV‐causing bacteria (Cutibacterium acnes and Staphylococcus epidermidis), creating significant inhibition zones (up to 5 mm), demonstrating their potential as promising therapeutic modality. Overall, plasma treatment enabled effective surface functionalization, producing antibacterial fibers with enhanced surface properties suitable for application in AV management.

Remote sensing plays a central role in providing qualitative and quantitative information on forest ecosystems for sustainable management and forest carbon inventory. The ESA BIOMASS satellite mission launched in 2025 introduces the first global P-band Synthetic Aperture Radar (SAR) system dedicated to forest structure and biomass, but its potential for refining tropical forest ecosystem maps remains largely unexplored. Here we compare P-band (~70cm wavelength) BIOMASS data with C-band Sentinel-1 SAR to assess their respective ability to discriminate structurally and hydrologically different forest ecosystems in Brazilian and Bolivian Amazonia, including terra firme forest, flooded forest and forest–wetland mosaics, in the presence of sparse solid ground-truth data.We use MapBiomas Amazonia and MapBiomas Bolivia as primary land-cover references, taking advantage of their annual time series from 1985 to 2024 to address label noise. Because these maps are not error-free at the pixel level, we develop a noise-labelling pre-processing workflow to derive high-confidence forest samples at the epoch of the first BIOMASS acquisitions (2025). The workflow combines (i) spatial homogeneity constraints (distance to class boundaries, neighbourhood purity, minimum patch size), (ii) temporal stability of the MapBiomas class history (to identify pixels with persistent forest or flooded forest trajectories), and (iii) physical plausibility checks using auxiliary optical and terrain indicators. Pixels that satisfy these criteria are retained as reliable proxies for different forest ecosystem types.For these filtered samples we extract BIOMASS Detected Ground-range Multi-looked (DGM) backscatter and Sentinel-1 Ground-range Detected (GRD) backscatter, derive polarisation ratios and simple texture metrics, and quantify within-class variability and between-class separability for both frequencies. We pay particular attention to forest–non-forest transitions and to distinctions among terra firme forest, flooded forest and adjacent forested wetlands that are relevant for high-carbon stock and peat-forming systems. Preliminary results from the Brazilian test site indicate that P-band reduces within-class variance in forested classes and enhances separability between terra firme and flooded forests compared to C-band alone, while C-band performs comparably or better for some open and anthropogenic land covers. By extending the analysis to Bolivian Amazonia and to a richer legend of forest and forest-wetland classes, and by testing a similar workflow for peatland-prone flooded forests, this study provides a first evaluation of the potentials and limitations of BIOMASS P-band SAR for tropical forest applications under sparse ground-truth conditions.

The exponential increase in global waste production resulting from population growth, urbanization, and increased consumption has brought waste management to the forefront of global environmental challenges. This paper provides an analysis of waste management practices in two contrasting case studies. Japan a highly developed country, exemplifies a model of advanced waste management with a robust regulatory framework, extensive public engagement, and sophisticated waste sorting, recycling, and recovery infrastructure, all underpinned by “ Mottainai ”—a concept deeply rooted in Japanese culture that emphasizes resource conservation and waste minimization. In contrast, Poland, like many Eastern European countries, faces systemic challenges, including inadequate infrastructure, limited recycling facilities, and low public awareness. Drawing on empirical evidence and scholarly discourse, this paper highlights the critical role of societal attitudes, policy interventions, and technological advances in shaping effective waste management strategies. By elucidating the key determinants of successful waste management systems, this paper contributes insights to the global discourse on sustainable development and informs future efforts toward sustainable waste management.

The identity of Melolontha pexa Zubkov, 1833 (Coleoptera: Scarabaeidae: Melolonthinae: Rhizotrogini) is clarified by the designation of a neotype. Melolontha pexa is transferred to the genus Holochelus Reitter, 1889 as Holochelus (Eriotrogus) pexus (Zubkov, 1833), new combination and proposed a senior subjective synonym of H. (Eriotrogus) aschhabadensis (Nonveiller, 1965) and H. (Holochelus) heptapotamicus Nikolajev, 2009. Although M. pexa Zubkov, 1833 is a primary junior homonym of Melolontha pexa Germar, 1823, its validity must be preserved according to the Article 23.9.5 of the International Code of Zoological Nomenclature (1999) and the case must be brought to the commission for a ruling. The type species of the genus Adoretops Kraatz, 1883 is designated as Demodema bonariensis Bruch, 1909, which was misidentified as Melolontha pexa Zubkov, 1833 in the original designation. This results in Adoretops becoming a new subjective junior synonym of Plectris LePeletier & Audinet-Serville, 1828 (Coleoptera: Scarabaeidae: Melolonthinae: Macrodactylini). The history of the beetle material collected by G.S. Karelin between 1827 and 1836 in the vicinity of the Caspian Sea is summarized and the collaboration of Karelin and Zubkov is discussed.

Soil materials and road pavement construction materials are subjected to dynamic and repetitive loading of different intensity from vehicle traffic. To account for the cyclic nature of material loading and its non-linear behavior, the concept of the resilient modulus has been adopted. It is a fundamental property of unbound pavement materials, as it quantifies the stiffness of the material under repeated loading. Its real value depends on the actual material parameters—e.g., maximum dry density, moisture content, compaction method, and the number and magnitude of repeated loads—and on the state of stress of the actual pavement structure. The resilient modulus value is therefore not constant for a given material type but varies over a certain interval depending on the above-mentioned parameters and the actual test conditions. Therefore, the method of determining the resilient modulus must take into account all the above factors. The standard method for resilient modulus determination is the cyclic triaxial test, but the cyclic CBR test procedure is also used. It uses standard California Bearing Ratio (CBR) test equipment, and thus it is a very simple and very economical testing method. In the presented paper, the influence of loading force and number of loading cycles on the deformation characteristic and resilient modulus of the analyzed soil is investigated. A total of 72 soil specimens are tested at two different levels of loading force and six different numbers of loading cycles. The obtained results confirm that the resilience modulus increases with increasing loading force value and with increasing number of loading cycles. For the soil analyzed, the resilient modulus ranges in the interval 31-83 MPa.

Climate change and increasing weather variability pose significant challenges to agricultural productivity, particularly in temperate regions. This study examines climate impacts on yields of barley, rapeseed, rye, and wheat across the Czech Republic (2016–2024) using meteorological data and evaluates three machine learning models (Random Forest, Gradient Boosting, Support Vector Regression) for yield prediction solely on meteorological variables. Three validation strategies were tested: random (70/30%), spatial (region-based), and temporal (last three years). Random partitioning yielded the highest accuracy, but risks data leakage and is therefore not recommended for forecasting. Spatial partitioning showed low to moderate accuracy, suggesting ML approaches can partially interpolate yields in areas with no data (interpolation), particularly for barley and wheat. Temporal partitioning caused a substantial drop in predictive skill, producing unreliable forecasts for rye and rapeseed and indicating that meteorological data alone are insufficient for robust yield prediction. Support Vector Regression produced consistently negative R 2 values across all validation strategies, demonstrating fundamental model-task incompatibility. Wheat showed highest accuracy (random: 72.57 ± 7.19%; spatial: 53.45 ± 19.31%; temporal: 25.10%), driven largely by Year (>50% importance). Without Year, spatial R 2 dropped to 0.2–27%, confirming limited predictive power of meteorological variables alone. Crop climate analysis revealed distinct responses: rye was highly heat-sensitive, wheat and rapeseed benefited from late-season warmth, excess soil moisture during dormancy reduced rapeseed and wheat yields, and barley responded positively to stable warm conditions. These findings emphasize the importance of choosing an appropriate validation approach for yield prediction, depending on the intended task (interpolation or forecasting), as well as the value of feature interpretability. Key limitations include reliance on meteorology-only predictors without management or soil data, a small temporal test set (3 years, 42 observations per crop), and models' strong dependence on Year and Region features that limit operational forecasting utility. • Rye sensitive to Tmin (r≈–0.56); excess soil moisture reduces rapeseed/wheat yields • Random partitioning inflates performance; use spatial/temporal validation instead • Meteorological features alone achieved low R; SVR produced negative R 2 across all strategies • Year was key for wheat; excluding it dropped R 2 from 73 ± 7% to 20 ± 48%

International trade poses a growing threat to global biosecurity, with bark- and wood-boring beetles representing a major concern for forest health. Non-native species are frequently introduced at points of entry, where populations can establish in the surrounding landscape. To improve early detection, generic surveillance programs use traps in these high-risk areas, collecting a broad spectrum of species. These traps also capture native beetles, providing insights into the potential species pool that could become exotic elsewhere. However, implementing effective landscape-wide surveillance within reasonable resource limits remains challenging. In this study, we used trapping data of Cerambycidae and Scolytinae from 11 high-risk areas across Europe and North America to develop practical recommendations for generic surveillance at multiple spatial scales. Specifically, we attempted to address two key questions: (1) how to maximize the single-trap efficacy depending on the trap surroundings; and (2) how many traps should be used in a landscape-wide sampling depending on landscape composition. Under budget constraints, we recommend prioritizing trap placement within forest patches and avoiding locations surrounded by roads or buildings. Urban-dominated landscapes required greater sampling effort (i.e., more traps) than forest-dominated landscapes. Deploying fewer than four traps per square kilometer might lead to an incomplete representation of the local bark- and wood-boring beetle community, losing about 30%-50% of species. Overall, our findings highlight the importance of incorporating landscape ecology into generic surveillance planning to optimize trap effectiveness within resource limitations.