Search

All-sky imaging systems are currently very popular. They are used in ground-based meteorological stations and as a crucial part of the weather monitors for autonomous robotic telescopes. Data from all-sky imaging cameras provide important information for controlling meteorological stations and telescopes, and they have specific characteristics different from widely-used imaging systems. A particularly promising and useful application of all-sky cameras is for remote sensing of cloud cover. Post-processing of the image data obtained from all-sky imaging cameras for automatic cloud detection and for cloud classification is a very demanding task. Accurate and rapid cloud detection can provide a good way to forecast weather events such as torrential rainfalls. However, the algorithms that are used must be specifically calibrated on data from the all-sky camera in order to set up an automatic cloud detection system. This paper presents an assessment of a modified k-means++ color-based segmentation algorithm specifically adjusted to the WILLIAM (WIde-field aLL-sky Image Analyzing Monitoring system) ground-based remote all-sky imaging system for cloud detection. The segmentation method is assessed in two different color-spaces (L*a*b and XYZ). Moreover, the proposed algorithm is tested on our public WMD database (WILLIAM Meteo Database) of annotated all-sky image data, which was created specifically for testing purposes. The WMD database is available for public use. In this paper, we present a comparison of selected color-spaces and assess their suitability for the cloud color segmentation based on all-sky images. In addition, we investigate the distribution of the segmented cloud phenomena present on the all-sky images based on the color-spaces channels. In the last part of this work, we propose and discuss the possible exploitation of the color-based k-means++ segmentation method as a preprocessing step towards cloud classification in all-sky images.

Geomorphological mapping with an emphasis on rock landforms was carried out in the Pohořská Mountains (Cz. Pohořská hornatina) and the positional data acquired were further processed using statistical and cartographical methods. The spatial distribution of rock landforms was investigated in relation to lithology, slope, orientation, and elevation based on an analysis using ArcGIS 9.1. The spatial distribution of rock landforms was primarily determined by the index of distribution Wij = Xi / Yj, where Xi is the percentage representation of landforms in the appropriate category and Yj is the percentage quotient of this category in the entire area studied, and wassecondarily determined according to the sum (sum distribution) of the arithmetic mean and the average deviation.

The by-product of the current global economic processes is increasing competitive pressure. In conditions of world economy globalization, the Czech Republic was particularly successful in the 1990s, mainly due to the strategy of “low cost economy”. Low costs were the primary source of competitive advantage. Increasing pressure of large countries with cheap labour force does not allow the Czech Republic make its labour costs lower and use this as a source of economic growth and international competitiveness. Therefore the Czech Republic was forced to direct its further economic development towards the knowledgebased economy. Due to this new priority, in the economic policy the innovative abilities of the companies, increasing quality of human resources, as well as research and development of the new technologies became the most important tasks. For the most of companies these trends were tantamount with the necessity of basic changes in production process organization, management, human resources development, etc. Both inner and outer entrepreneurial environments were thus considerably changed and innovative companies become the carriers of comparative advantages in the regional development. The objective of the article is to assess the current position of the Czech Republic in international perspective as far as the innovative efficiency is concerned. The authors also will try to point the strong and weak sides of the innovation system in the Czech Republic. The second part of the paper analyses the innovative potential of particular Czech regions.

Innovations are presently considered one of the most important factors of the regional development. Their introduction enhances the competitiveness of the region, economic growth and living standard in regions. The objective of the contribution is to assess regional innovative potential of the Zlínský region, to present the results of enquiry aimed at the analysis of conditions and hinterland for the innovative enterprise in the Zlínský region and to identify the demands of entrepreneurial subjects in R&D.

Background: Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus is still a very dangerous and life-threatening disease with an extremely heterogeneous course. Older patients and those with comorbidities are at increased risk of death from the disease but young patients can develop potentially lethal complications too. For those reasons, numerous recent studies focus on the analysis of markers associated with early assessment of COVID-19 prognosis. Previous publications provided evidence for the Intensive Care Infection Score (ICIS) as an easy to use tool to assess the risk for bacterial infection in ICU patients based on a combination of haematologic parameters. This study evaluated the performance of ICIS as a prognostic marker of stages of disease in COVID-19 patients. Methods: A total of 205 COVID-19 patients admitted to the University Hospital Hradec Kralove, Czech Republic, with symptoms of respiratory tract infection and a positive RT-PCR test for SARS-CoV-2 virus were enrolled in this study. Forty-nine patients developed mild COVID-19 symptoms (no oxygen therapy needed), 156 patients developed moderate or severe symptoms (supplemental oxygen therapy or death). Results: ICIS predicted the mild or moderate/severe course with the highest AUC (0.773). The cut-off value (ICIS = 3.5) was selected as the value with the highest Youden index (0.423). The cut-off value could predict a mild or moderate/severe course of the disease with the highest specificity (77.6%) and positive predictive value (90.2%) of all markers used in this study. Sensitivity was 64.7%. Conclusion: ICIS is a reliable, cheap, fast and simply interpretable score for the early identification of moderate/severe course of COVID-19 in an early stage of the disease. ICIS> 3 predicts a severe course of the disease with high specificity and positive predictive value.

The main aim of this work is to find simple criteria for automatic recognition of several meteorological phenomena using optical digital sensors (e.g., Wide-Field cameras, automatic DSLR cameras or robotic telescopes). The output of those sensors is commonly represented in RGB channels containing information about both colour and luminosity even when normalised. Transformation into other colour spaces (e.g., CIE 1931 xyz, CIE L*a*b*, YCbCr) can separate colour from luminosity, which is especially useful in the image processing of automatic cloud boundary recognition. Different colour transformations provide different sectorization of cloudy images. Hence, the analysed meteorological phenomena (cloud types, clear sky) project differently into the colour diagrams of each international colour systems. In such diagrams, statistical tools can be applied in search of criteria which could determine clear sky from a covered one and possibly even perform a meteorological classification of cloud types. For the purpose of this work, a database of sky images (both clear and cloudy), with emphasis on a variety of different observation conditions (e.g., time, altitude, solar angle, etc.) was acquired. The effectiveness of several colour transformations for meteorological application is discussed and the representation of different clouds (or clear sky) in those colour systems is analysed. Utilisation of this algorithm would be useful in all-sky surveys, supplementary meteorological observations, solar cell effectiveness predictions or daytime astronomical solar observations.

VZLUSAT-1 nanosatellite (scheduled launch in spring 2017 from India) is a CubeSat mission which, besides other instrumentation, contains X-ray desk to perform efficiency tests of the X-ray optics. In this article the analysis of potential observational candidates for VZLUSAT-1 X-ray board is presented together with the suggestion of observational modes, laboratory measurements, and estimations of exposure settings.