Strategic Financial Analysis and Evaluation of a Selected Company

Tato diplomová práce se zabývá strategickou finanční analýzou společnosti TOLFIN s.r.o. Byla provedena analýza současného stavu a také predikce do roku 2028. V této práci byly použity poměrové ukazatele, predikční a bankrotní modely. Cílem této práce je provést analýzu společnosti a navrhnout řešení, které povedou ke zvýšení rentability a výkonnosti v následujících letech.This diploma thesis focused on the strategic financial analysis of the company TOLFIN s.r.o.The analysis of the current situation was made and also prediction until 2028. In this thesis were used proportion rations, prediction and bankruptcy models. The goal of this thesis is to made an analysis of the company and propose a solution, which will lead to increase profitability and performance in the coming years.634 - Katedra ekonomiky a managementu v průmysluvýborn

Multifaceted activity of cytokinin in leaf development shapes its size and structure in Arabidopsis

The phytohormone cytokinin has been shown to affect many aspects of plant development ranging from the regulation of the shoot apical meristem to leaf senescence. However, some studies have reported contradictory effects of cytokinin on leaf physiology. Therefore cytokinin treatments cause both chlorosis and increased greening and both lead to decrease or increase in cell size. To elucidate this multifaceted role of cytokinin in leaf development, we have employed a system of temporal controls over the cytokinin pool and investigated the consequences of modulated cytokinin levels in the third leaf of Arabidopsis. We show that, at the cell proliferation phase, cytokinin is needed to maintain cell proliferation by blocking the transition to cell expansion and the onset of photosynthesis. Transcriptome profiling revealed regulation by cytokinin of a gene suite previously shown to affect cell proliferation and expansion and thereby a molecular mechanism by which cytokinin modulates a molecular network underlying the cellular responses. During the cell expansion phase, cytokinin stimulates cell expansion and differentiation. Consequently, a cytokinin excess at the cell expansion phase results in an increased leaf and rosette size fueled by higher cell expansion rate, yielding higher shoot biomass. Proteome profiling revealed the stimulation of primary metabolism by cytokinin, in line with an increased sugar content that is expected to increase turgor pressure, representing the driving force of cell expansion. Therefore, the developmental timing of cytokinin content fluctuations, together with a tight control of primary metabolism, is a key factor mediating transitions from cell proliferation to cell expansion in leaves

Data Vizualization with Focus on Financial Markets

Tématem této bakalářské práce je vizualizace dat se zaměřením na finančních trzích při intradenním obchodování komodit a aplikování obchodní strategie. V první části jsou nastíněny vizualizace dat pomocí indikátorů. Z těchto poznatků je vytvořena strategie, která je otestována na historických datech. Strategie jsou aplikovány pomocí CFD kontraktů na trhu zlata a ropy WTI, následně komparovány a nejvýnosnější strategie podrobena demo obchodování.The topic of this bachelor thesis is Data Visualization with Focus on Financial Markets for intraday commodity trading and application on trading strategy. In the first part are outlined data visualizations by using indicators. From these findings are created strategies, which are tested on historical data. The strategies are applied using CFD contracts on commodities, then compared, and the most efficient strategy is subjected to demo trading.545 - Katedra ekonomiky a systémů řízenívýborn

Influence of Bias Correction Methods on Simulated Köppen−Geiger Climate Zones in Europe

Our goal was to investigate the influence of bias correction methods on climate simulations over the European domain. We calculated the Köppen−Geiger climate classification using five individual regional climate models (RCM) of the ENSEMBLES project in the European domain during the period 1961−1990. The simulated precipitation and temperature data were corrected using the European daily high-resolution gridded dataset (E-OBS) observed data by five methods: (i) the empirical quantile mapping of precipitation and temperature, (ii) the quantile mapping of precipitation and temperature based on gamma and Generalized Pareto Distribution of precipitation, (iii) local intensity scaling, (iv) the power transformation of precipitation and (v) the variance scaling of temperature bias corrections. The individual bias correction methods had a significant effect on the climate classification, but the degree of this effect varied among the RCMs. Our results on the performance of bias correction differ from previous results described in the literature where these corrections were implemented over river catchments. We conclude that the effect of bias correction may depend on the region of model domain. These results suggest that distribution free bias correction approaches are the most suitable for large domain sizes such as the pan-European domain

Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences

Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference

Czech Drought Monitor System for monitoring and forecasting of agricultural drought and drought impacts

The awareness of drought and its impacts on Central Europe increased after the significant drought episodes in 2000, 2003, 2012 and 2015, which were all estimated to have caused over 500 million Euro in damage in the Czech Republic alone. These events indicated the need for timely and highresolution monitoring tools that would enable analysing, monitoring and forecasting of drought events. Monitoring soil water availability in near real time and at high-resolution (up to 0.5 × 0.5 km for some products) helps farmers and water managers to mitigate impacts of these extreme events. The Czech Drought Monitor was developed between 2012 and 2014 and has since been operational as an online platform. It uses an operational modelling system that consists of four pillars: (a) weekly soil moisture estimates based on spaceborne Advanced Scatterometer sensor measurements; (b) the daily SoilClim soil moisture model, which runs based on high-density network data from the Czech Hydrometeorological Institute with a 55-year reference period; (c) weekly reports on vegetation condition, which is deduced from satellite-based vegetation indices and early warnings of imminent drought impacts; and (d) weekly reports of soil moisture, especially after drought impacts, which are provided by dozens of experts. Since 2016 the drought forecast (+9 days) has been released daily based on an ensemble of five numerical weather prediction models combined with a weekly drought outlook (+2 months). The analysis of four recent episodes (2000, 2003, 2012 and 2015) clearly showed that both large-scale and regionally restrained drought episodes posed serious risks in terms of their impacts and damage. Comparisons with historical droughts showed that these events, especially the 2000, 2003 and 2015 events, were among the top five drought episodes in the June–August period observed in the Czech Republic since 1961 in terms of spatial extent, magnitude and duration

Drought Prediction System for Central Europe and Its Validation

In recent years, two drought monitoring systems have been developed in the Czech Republic based on the SoilClim and AVISO soil moisture models. The former is run by Mendel University and Global Change Research Institute (CAS), while the latter, by the Czech Hydrometeorological Institute. SoilClim is based more on real soil properties and aimed primarily at agriculture, while AVISO complements the system with more theoretical presumptions about soil, showing, rather, climatological potential. Both soil moisture models were complemented by forecasts on a daily basis, taking meteorological inputs from NWP (Numerical Weather Prediction) models and thus giving short- to mid-range outlooks up to 9 days ahead. Validation of the soil moisture and drought intensity prediction was performed and is presented in this article showing its prediction reliability and potential. In the analysis, we focus mainly on the past year, 2017. The tool has strong predictive power for soil moisture and drought intensity so it is suitable for farmers who need to make decisions about irrigation and production activities. The presented system is fully functional and can be applied in the coming years