The study examined the relationship between socio-demographic and occupational factors and the level of occupational burnout using the dimensions of emotional exhaustion (EE), depersonalization (DEP), and personal accomplishment (PA). It examined 560 nurses working in hospitals and primary healthcare units. We used: Maslach Burnout Inventory and a questionnaire including socio-demographic (sex, age, marital status, education, parental status) and occupational (period of employment, workplace, managerial functions, additional employment) factors. An average respondent was 38.13 (SD = 10.16) and had a BA degree (56.0%). The respondents reported average values of the EE (22.8), a low level of DEP (Me = 6), and a low PA (27.63). Nurses working on the intensive care unit had a chance of a high level of DEP that was 75% lower (OR = 0.25, 95% CI = 0.13–0.50) than nurses working in conservative treatment units. Additional employment increased the risk of a high level of DEP (OR = 2.86, 95% CI = 1.70–4.84). The chance of low PA was 64% lower in the case of nurse managers (OR = 0.36, 95% CI = 0.13–0.998) than other nurses. Education, period of employment, additional employment, and managerial position had a significant influence on the level of occupational burnout. An analysis of nurses’ work overload and additional employment can be an interesting research area.

Concrete is one of the most frequently used building materials. Since failure of concrete can have catastrophic consequences, understanding its properties is a prerequisite to wide-spread application. In particular, mechanical tests are applied to investigate the cracking behaviour of concrete. Computed tomography (CT) is used to analyze the concrete’s microstructure non-destructively. Segmenting cracks in CT images is challenging. Crack structures may be very thin and, therefore, insufficiently resolved in the image data. Additionally, concrete is a heterogeneous composite material typically consisting of cement paste, aggregates, pores, and reinforcements. Hence, cracks have to be distinguished from these components. The immense size of representative image data (up to 2,000^2x10,000 voxels for a concrete beam) prohibits manual processing. We compare several methods for automatic crack segmentation. To enable a fair comparison, we design a comparative study based on simulated data. Simulated cracks of varying width and shape are integrated in CT images of concrete to achieve realistic images. Using these data, machine learning methods can be trained for the segmentation. Additionally, the existence of a ground truth allows for an objective evaluation of the results. The best segmentation results are obtained by a convolutional neural network (U-net) and by Hessian-based percolation. These two methods are applied to real CT scans. There we observe an additional challenge: The thickness of cracks varies continuously while the crack propagates. Hence, multi-scale approaches covering crack widths between 1 and 20 voxels are explored. On large images, voxel-wise crack segmentation on the whole image is not feasible from a computational point of view. Hence, we suggest to initially roughly scan the image for regions that are likely to contain cracks. Then, crack segmentation is restricted to those regions. Applicability, limitations, and robustness of our methods are discussed.

In recent years, organic food, produced with the use of natural means and production methods, has been gaining more and more popularity among consumers. This is due, inter alia, to their belief that it is more abundant in health-promoting bioactive compounds and safer than conventional food. Consumers are increasingly aware of the harmfulness of plant protection products used in intensive agriculture, which are not allowed in organic production. At the same time, it is reported that a certain share of organic products on the EU market are contaminated with pesticide residues, which may raise consumer concerns and lead to a loss of trust in organic food. The aim of the present study was to investigate the problem of pesticide residues occurrence in random samples of organically produced fruits and vegetables (apples, potatoes, carrots, and beetroots) commonly used in the Polish households, and which are available directly from the organic producers in open markets in Poland. For simultaneous analysis of 375 pesticides, an LC-MS/MS system consisting of an Eksigent expert ultraLC 100-XL coupled to a triple quadrupole mass spectrometer QTRAP 6500 and GC Agilent 6890 N equipped with ECD/NPD system were used. Among the 96 vegetable and fruit samples studied, 89 samples (92.7%) were free from detectable pesticide residues, 7 samples (7.3%) of carrot (5) and potato (2) were contaminated, and in 1 of them (1.0%) the detected residues exceeded the maximum residue limit (MRL). None of the tested apple and beetroot samples were found to contain detectable residues. These findings are important for Polish consumers who look for high-quality organic food. However, the presence of detectable residues in a small proportion of the organic samples indicates a need to strengthen the monitoring of pesticides in organic crops, to educate farmers and to raise their awareness regarding the risks of unauthorized use of pesticides banned in organic farming, which can damage the reputation of the whole organic sector.

In recent decades, organic farming based on natural means and methods of production is gaining more and more popularity. It is due to the growing awareness of the society regarding the harmfulness of chemicals used in intensive agriculture, which influences the growing interest of both producers and consumers in organic food. Searching for plant cultivars performing best under organic management in terms of crop quality is one of the important research topics of the recent years. The aim of the present study was therefore to compare 8 oat and 17 barley cultivars grown in identical organic production conditions in terms of polyphenols and carotenoids contents and the mycotoxins contamination in grains. The analyses of bioactive compounds were performed using HPLC and the mycotoxins occurrence using LC-MS/MS methods. Among the barley cultivars studied, the grains of SU Lolek, Rubaszek and Podarek accumulated the highest content of polyphenols. Grain of Soldo cultivar was richest in carotenoids, but at the same time most of the mycotoxins identified within the study were found in the grains of this cultivar. In the case of oat, the highest content of polyphenols was found in the grain of Harnaś, Nawigator and Arden cultivars, while Pascal and Amant grain was richest in carotenoids. Among all the oat cultivars tested, only Amant grain was free from the studied mycotoxins. These findings are important for producers as well as consumers, who search for quality organic foods.

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