Pavel Tomášek

Compliance with the principles of occupational safety and health is one of the preconditions for avoiding emergency events. Without a doubt, this is one of the underlying duties of every employer or operator of waste recycling centers. The risk to health resulting from an orphan source of ionizing radiation in the form of scrap metal is quite high and can affect a significant number of people. As part of the precautions relating to employees, the employer must analyze the risks associated with working with scrap metal and emphasize the possibility of any previous contamination. In practice, various methods and procedures are used to assess the risks at scrap metal collection facilities, which are mostly based on the knowledge and experience of their inspectors. However, this is not usually done by means of appropriate risk analysis methods, which is the main disadvantage. The goal of this article is to point at the risks related to discovering an orphan source of ionizing radiation by using the Ishikawa diagram and the point method. Furthermore, this article also deals with the demarcation of a safety zone with respect to the protection of health and the environment. The specification of risks, the proposal of recommended precautions, and the expeditious demarcation of a safety zone in the case of the intervention of fire brigades in order to protect and/or decontaminate the persons are also included. Doi: 10.28991/CEJ-2022-08-11-013 Full Text: PDF

This article is focused on the creation of a data model of an information system to support the processes of population sheltering (PoS). PoS processes have undergone significant changes in the Czech Republic. The PoS system was transformed for the use of improvised shelters. For sustainable PoS, it is necessary to standardize the approach of crisis management staff. Key research hypotheses: the data model must respect the methodological framework of planning and implementation of the PoS, especially when used for the design and implementation of the improvised shelters; the standardization of residential buildings can be used to facilitate and streamline the sustainable process of planning of the PoS; the unified data model of the PoS can be considered suitable for standardization and development in the field of sustainable planning and implementation of the PoS. Softwares such as SW QGIS desktop 2.6.0, OpenStreetMap (OSM) spatial database, ESRI shapefile format, and S-JTSK/Krovak East North 5514 coordinate system were used for the verification of the spatial data model, and ASP.NET MVC 4.7.2 Microsoft SQL Server Express 2019, jQuery 3.3.1, Bootstrap 3.4.1, Kendo UI 2019.1.220, and virtual Windows Server 2016 were used for the implementation of the physical data model. The physical data model substantiates the benefits.

High quality pseudorandom number generators were needed in many software solutions throughout the history of programming. Nowadays, these generators play an even more significant role in software development. Generally, these generators bring a certain level of coincidence in some algorithms which need it. This work focuses on the statistical evaluation of one of the representatives of the generators using Pearson's Chi-square goodness of fit test. The generator of pseudorandom numbers under test is the specific implementation in the modern standard of the programming language of C++ (the standard of C++17). Results presented in this paper inform whether the numbers generated by the selected generator follow the desired probability distribution (normal).

Camera traps are increasingly one of the fundamental pillars of environmental monitoring and management. Even outside the scientific community, thousands of camera traps in the hands of citizens may offer valuable data on terrestrial vertebrate fauna, bycatch data in particular, when guided according to already employed standards. This provides a promising setting for Citizen Science initiatives. Here, we suggest a possible pathway for isolated observations to be aggregated into a single database that respects the existing standards (with a proposed extension). Our approach aims to show a new perspective and to update the recent progress in engaging the enthusiasm of citizen scientists and in including machine learning processes into image classification in camera trap research. This approach (combining machine learning and the input from citizen scientists) may significantly assist in streamlining the processing of camera trap data while simultaneously raising public environmental awareness. We have thus developed a conceptual framework and analytical concept for a web-based camera trap database, incorporating the above-mentioned aspects that respect a combination of the roles of experts’ and citizens’ evaluations, the way of training a neural network and adding a taxon complexity index. This initiative could well serve scientists and the general public, as well as assisting public authorities to efficiently set spatially and temporarily well-targeted conservation policies.