Jiří Neubauer

Oxbow lakes and their riparian habitats are dynamic but vulnerable ecosystems in intensively modified European landscapes, yet their acoustic ecology remains poorly explored. This study presents their first year-round ecoacoustic monitoring. We surveyed seven man-made oxbow lakes in the Lower Morava River Basin (Czechia) from March 2022 to February 2023 to examine seasonal and diurnal patterns of the soundscape, the effects of conservation status, and the role of surrounding land use. We compared two acoustic indices—the Acoustic Complexity Index (ACI) and the Mid-Frequency Cover (MFC)—as proxies for avian biophony, anchored by manual aural inspection of 1,380 stratified one-minute recordings. Generalized additive models revealed clear intra-annual and diurnal structuring of the soundscape, with a dominant morning peak—and a weaker secondary afternoon rise in some months—most pronounced from late winter through spring. October stood out with an increase in species richness and MFC, reflecting intensified stopover use during autumn migration. MFC tracked avian species richness more closely than ACI, while ACI remained more sensitive to local acoustic context. Protected oxbow lakes consistently exhibited higher acoustic index values and greater cumulative species richness than unprotected sites, while landscape gradients, particularly road proximity and urban cover, strongly shaped the assemblages. Year-round passive acoustic monitoring captured both natural phenological shifts and anthropogenic pressures. We recommend MFC as the more reliable biophony-tracking index for these soundscapes, with ACI as a complementary context-sensitive metric, offering a scalable tool for biodiversity assessment and restoration of small wetland systems in human-dominated landscapes.

Forest fires have historically been a concern in fire-prone regions, where climatic and environmental conditions naturally foster fire occurrences. However, in Central Europe, the increasing frequency of wildfires is driven by a complex interplay of factors, reflecting both environmental changes and human influences. This study uses machine learning to address key challenges in understanding fire risk in low-risk regions, specifically: the limited applicability of models developed for high-risk areas and the struggle to incorporate dynamic spatiotemporal factors. By analyzing eight years of fire data alongside variables such as weather, geography, and population density, our objective is to develop a machine learning and GIS-based procedure for identifying and understanding the underlying complexity of fire risk in Czechia. K-Medoids clustering revealed challenges in forming distinct clusters, suggesting the need for additional or more refined predictors. Classification models (e.g. Random Forest, Gradient Boosting, k-Nearest Neighbors) were used to predict periods of increased fire risk. Soil temperature and population density emerged as the most significant predictors. This study proposed two procedures: one that combines clustering and bivariate analysis to identify clusters with homogeneous factors, and another as a classification-bivariate visualization framework for assessing fire risk. The results indicate that classification performance can serve as a proxy for spatial complexity and highlight regions where multiple factors drive fire occurrence. To enhance model accuracy and applicability, incorporating high-resolution weather data, population mobility patterns, vegetation types, and advanced modeling techniques is recommended. Introducing advanced predictors could help differentiate between fires driven by natural factors and those influenced by human activities.

Meteorological conditions exert a considerable influence on traffic patterns. This paper examines the influence of meteorological variables on the daily number of?traffic accidents requiring fire brigade intervention. The influence of meteorol...

The development of new technologies (e.g., three-axial accelerometers) and their implementation to the armed forces made it possible to rather easily obtain data on transportation with regard to cargo securing. It is, however, crucial to evaluate the data, and identify the factors important for the selection of an appropriate cargo securing system. Particular attention should be paid to the transportation on low-quality roads, or possibly in the terrain as it can be expected that the cargo (as well as the vehicle and the driver) is subjected to greater shocks. Data obtained from the military transport experiment have been processed using advanced statistical methods (a contaminated probability distribution, methods of statistical comparison, and an analysis of variance). The transportation data from the use of two military trucks on three different road surfaces has been compared. The results show, in which axes there are statistically significant differences in terms of the frequency of exceeding normatively determined acceleration coefficient values in relation to the road surface type and the vehicle type. Further results are based on the modeling of acceleration coefficients using a contaminated log-normal distribution, where the distribution of acceleration coefficient values is contaminated by outliers resulting from sudden changes in the transport conditions.

The aim of the article is to identify possible “follower” behaviour; it means to reveal countries following the average military burden of North Atlantic Treaty Organization (NATO) member states. To analyse the relationship between military expenditure of NATO member states and selected socio-economic variables (average military burden of NATO member states, gross domestic product, government expenditure (non-military), share of trade balance of gross domestic product and population), the Autoregressive Distributed Lag model has been used. The short-term results demonstrate follower behaviour, especially in the new NATO member states (effort to fulfil the commitment to spend 2% of gross domestic product on defence). The long-term results have revealed a positive relationship between military expenditures and the variable describing the average military burden of NATO member states in the traditional and also new NATO member states. A positive relationship between military expenditure and gross domestic product has also been observed in the majority of evaluated countries in the long-term model. The public good effect has been determined in six member states only, and the crowding out effect in five member states. A positive effect of the balance of trade on military expenditure has been observed in two countries only.

This article presents the possibilities in solving the Weighted Multi-Facility Location Problem and its related optimization tasks using a widely available office software—MS Excel with the Solver add-in. To verify the proposed technique, a set of benchmark instances with various point topologies (regular, combination of regular and random, and random) was designed. The optimization results are compared with results achieved by a metaheuristic algorithm based on simulated annealing principles. The influence of the hardware configuration on the performance achieved by MS Excel Solver is also examined and discussed from both the execution time and accuracy perspectives. The experiments showed that this widely available office software is practical for solving even relatively complex optimization tasks (Weighted Multi-Facility Location Problem with 100 points and 20 centers, which consists of 40 continuous optimization variables in two-dimensional space) with sufficient quality for many real-world applications. The method used is described in detail and step-by-step using an example.

The article focuses on evaluating a transportation experiment that intends to improve road safety by analyzing transport shocks that significantly affect the system of securing the load, vehicle, driver, and other aspects. Within Europe, improper or insufficient securing of loads causes up to 25% of all cargo vehicle accidents. Our transport experiment consists of eight rides of a Tatra truck. The measured values of shocks (acceleration coefficients) are statistically evaluated. Three hypotheses are established for these purposes. First, it is proven that the probability distributions of the shocks values differ statistically significantly among individual rides, namely in their shape and median value. Thus further statistical analyses are performed separately for the individual rides, axes, and the accelerometer locations. These analyses prove significant exceedances of the normatively set limits given by the EN 12195-1:2010 standard, which is potentially risky. Especially for the z axis and y axis, the set 20% limit was exceeded in 75.0% and 56.3% of cases, respectively. In the case of the x axis, the established 20% limit was practically not exceeded at all. The analysis of exceeding the permitted limits (the statistical evaluation of the second and third established hypothesis) is based on boxplots that graphically describe the individual rides, as well as on the found contaminated log-normal distribution of the values of the acceleration coefficients in the individual rides. The last hypothesis regarding exceeding the double value of the permitted limit is rejected. Methods of statistical comparison are used during data analysis. The probability distribution of acceleration coefficients is modeled using a contaminated log-normal distribution.