Search

In recent years, nanoparticles have emerged as an important player in a broad range of applications, especially thanks to recent advances in their synthesis. The silver and copper nanoparticles are often used due to their antibacterial and fungicidal activities, and this article presents the results of the nanoparticle synthesis using electrical glow discharge generated directly in a volume of their salt solutions. Therefore, there is no influence of air (i.e. reactive nitrogen species) as it is usual in other commonly used approaches. Nanoparticles were prepared under various experimental conditions, and they were characterized by ultraviolet/visible spectrometry, dynamic light scattering, X-ray photoelectron spectroscopy, and high-resolution scanning electron microscopy. Particles were produced without any surfactant or stabilizing agent, and some of them showed higher resistance against agglomeration during their short-term (days) storage. The nanoparticle formation mechanism was confirmed by the fast camera imaging. Thus, the developed approach can be applied for simple environmentally friendly nanoparticle production for various applications.

Research background: Bankruptcy and fnancial distress prediction has always been an integral part of any fnancial management system. It gives an indication to stakeholders to take precautionary measures in order to avoid losses. Te traditional approaches for prediction, including logistic regression and discriminant analysis, are constrained by their inability to deal with complex and high-dimensional data (Odom and Sharda, 1990; Min and Lee, 2005). Recent developments in the feld of machine learning, and particularly autonomous learning classifers, present a potential proposed alternative. Purpose: Te purpose of this paper is to propose a frst-order autonomous learning classifer (F-O ALMM0) for predicting bankruptcy of business entities and individuals. Design/methodology/approach: Te data fle contained a total of 352 companies obtained from the Kaggle database and incorporating 83 fnancial ratios. Initially, the model's performance was assessed as a preliminary step, but the results were average, followed by the application of Principal Component Analysis (PCA) to enhance the quality of the input’s variables. Aferwards, the number of independent variables was reduced to 26. Tus, the results were improved. [ABSTRACT FROM AUTHOR]

Within the paper submitted, the author seeks the perspective for an investor, entrepreneur or anybody who does or plans to do business in the sector of transportation and warehousing and might need a method to determine the impact of sales and value added on earnings. The data are classified according to the CZ NACE coding for the transport and warehousing sectors. The complete data from the accounting records in the given field for the years 2016-2020 are selected. The mutual relationship of both parameters is determined using correlation. According to the results, in individual years, the correlation of both parameters and earnings differs, with each parameter showing a different impact on earnings in terms of individual years, type of company or region in which a given company operates. Overall, however, the research can be influenced by the given data, which need to be processed in more detail for possible further investigation of the given issue. The data were divided into parameters of individual years, regions, and the type of legal person.

The usage of artificial intelligence as business opportunities on the markets has a wide potential for many fields. Due to the rapid development of technology, artificial intelligence is quite expensive but useful. The research is done by making basic SWOT analysis (analysis Strenghts-Weaknesses-Opportunities-Threats) of the R&D (Research and Development) stage in the Czech Republic and by document analysis from articles on Web of Science and Scopus. The results shows, that the AI is beneficial for large number of fields and that the AI (Artificial Intelligence) has a great potential for implicating this in business, healthcare, economics and many more. The AI has potential to improve people’s life and work.

Introduction: As a result of climate change and bark beetle outbreaks in forests, extensive salvage, and sanitary cutting have been undertaken in Europe, resulting in many clear-cuts with coarse stumps. The populations of pine weevils (Hylobius abietis and Hylobius pinastri) are steadily increasing. A high abundance of pine weevils feeding on seedlings typically results in significant economic losses and prolongs the forest establishment period. However, information on these species spatial distribution and habitat selection patterns, necessary for assessing their harmfulness and subsequently accurately estimating their threat to reforested areas, is lacking. To determine which factors influence spatial selection patterns and seedling type preference, this study investigated which clear-cut factors increase pine weevil abundance and which seedling types (species and age) are the most preferred. Methods: The experiment was carried out on 20 clear-cuts in the central Czech Republic. We evaluated soil moisture level, average stump distance and diameter, proportion of other conifers, and whether the stumps were mulched. We detected the abundance of pine weevils using pitfall traps. We determined the feeding scar intensity on the first 10 cm of seedling stems on commonly used seedlings for reforestation in central Europe: 1-year-old Scots pine (Pinus sylvestris), European larch (Larix decidua), and 3-year-old Norway spruce (Picea abies), with 1,200 seedlings in total. The individual seedling types alternated in rows. The results were evaluated using generalized linear mixed-effect models (GLMMs). As dependent variables we used total numbers of H. abietis and H. pinastri. The soil moisture level, mulching, proportion of other conifers, average stump diameter, and distance were considered independent variables. Results: We found that Norway spruce was the least attractive to pine weevils. We found a higher abundance of H. abietis females in moist clear-cuts, confirming that dry clear-cuts are less suitable for oviposition. Conclusion: According to our findings, if foresters want to plant coniferous seedlings, it seems that planting older spruce is a better option than planting larch and pine. These findings provide valuable insights for forest management and reforestation strategies, equipping foresters with the knowledge to mitigate the threat of pine weevils and ensure successful forest establishment. [ABSTRACT FROM AUTHOR]

The paper deals with the the development of a specific company’s stock price time series. The aim of the paper is to use the time series method for a detailed analysis and evaluation of the development of Apple Inc. stock prices. Daily data from 2000 to 2020, daily data from the period of the economic crisis between 2007 and 2009 and daily data from the Covid-19 pandemic period from March 2020 to the end of the same year are used. The data, from the period of 2000 - 2020 show a gradual increase in Apple’s stock prices. The most common factor leading to the increase in stock prices is the launch of a new product or service on the global market. On the contrary, the reason for the decline in stock prices is customer dissatisfaction, the excess of demand over supply, or the political situation. The analysis of time series for the period of the economic crisis points to the fact that thanks to the development, innovation and constant introduction of new products into the market, the company was not significantly affected by the crisis and neither were stock prices. Naturally, there were some fluctuations in prices, but at the end of 2009, the company even reached the highest stock prices in its history to date. The analysis of time series during the global pandemic of Covid-19 shows a steady rise in stock prices. Currently, the company sells more and more products and introduces new services that help us work, study or entertain ourselves in these difficult times, in the safety of our homes.

Research background: Stock exchange trading is an activity carried out in order to achieve a profit. The oldest and largest market operator in the CR is the Prague Stock Exchange. The complex development of the market in a given period with regard to its development trends is monitored by means of the stock market index. The index of the Prague Stock Exchange is PX index. Purpose of the article: The objective of the contribution is the evaluation of the development of the PX index in the years 2018-2020 and the prediction of its further development. Methods: The data on the PX index were obtained from the official Prague Stock Exchange websites. The data are available for the period of 26 March 2018-31 March 2021. The processed data are analysed using neural networks, specifically the time series analysis. The opening price is used as a variable. Findings & Value added: The research results show that the Czech market index has been relatively stable in the past, its values being around its initial value, 1,000 points. No major fluctuations were recorded, as the PX index included very stable firms. However, with the onset of the COVID-19 pandemic, there was a sharp decline caused by the effect of anti-pandemic measures on the economy. Currently, the Czech market index is expected to grow gradually and stabilize at around 1,000 points.

Copyright of Retos, Revista de Ciencias Administrativas y Económicas is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of business is the company value which depends on its ability to generate future profits. It means that all the future decisions must be1 adopted with regard to the influence on the company value. The subject of interest in this paper concerns forestry and logging companies, particularly financial statements of companies that were active on the Czech market in 2016 and thus showed a profit. Aim of this paper is to identify particular value generators of forestry and logging companies in the Czech Republic in 2016. For this reason, the best possible methodology was developed to determine them and the objective was eventually achieved. Using artificial neural networks, a total of 14 indicators, entering a company value creation process most frequently, were identified and measured by the EVA equity indicator. The following key variables were identified as company value generators: external sources, long-term liabilities, material and energy consumption, other operating expenses and sales of goods.

The objective of the contribution is to identify a possible relationship between the development of the price of Brent oil (Brent in USD/barrel) and the CNY / USD Exchange rate by means of artificial neural networks. Understanding future fluctuation characteristics and the trend in oil prices is the basis for a deep understanding of systemic mechanisms and trends in related research areas. However, given the complexities of oil prices, it is very difficult to obtain accurate forecasts. Within the experiment, a total of 50,000 artificial RBF neural networks were generated. Was found the CNY / USD price will play a significant role in creating China's real product. Given that it was already proven that the CNY / USD exchange depends on Brent in USD / barrel, it is important to focus the further research on finding out the time lag with which the price of Brent in USD / barrel is actually reflected in the price of CNY / USD.

Despite widespread skepticism linked to cryptocurrencies, they are constantly gaining the interest of scholars, investors, media, and regulators. Recognizing the importance that portfolio risk maintains for crypto participants, this study attempts to shed light on this issue. We investigate the risk-return tradeoffs of the most tradable cryptocurrencies based on portfolio diversification techniques. Three different crypto portfolios containing a diverse number of cryptocurrencies were created to analyze the diversification risk from a historical perspective. Data concerning daily prices and their trade volume was collected from the Coin Market Cap database and covered the period from 1 January 2016 to 31 December 2022. The results regarding the risk-reward tradeoff stand in line with the portfolio theory, where higher expected returns offset higher risk. On average, the portfolio composed of 10 cryptocurrencies offers better optimization than the one with five, as it generates the same returns with lower risk. The year 2018 reflects the maximum diversification benefits in the three portfolios, corresponding to the period when cryptocurrencies gained massive popularity. From the managerial perspective, results inform crypto and institutional investors of the possible diversification benefits of the 15 most traded cryptocurrencies. [ABSTRACT FROM AUTHOR]

The price of oil is nowadays a hot topic as it affects many areas of the world economy. The price of oil also plays an essential role in how the economic situation is currently developing (such as the COVID-19 pandemic, inflation and others) or the political situation in surrounding countries. The paper aims to predict the oil price movement in stock markets and to what extent the COVID-19 pandemic has affected stock markets. The experiment measures the price of oil from 2000 to 2022. Time-series-smoothing techniques for calculating the results involve multilayer perceptron (MLP) networks and radial basis function (RBF) neural networks. Statistica 13 software, version 13.0 forecasts the oil price movement. MLP networks deliver better performance than RBF networks and are applicable in practice. The results showed that the correlation coefficient values of all neural structures and data sets were higher than 0.973 in all cases, indicating only minimal differences between neural networks. Therefore, we must validate the prediction for the next 20 trading days. After the validation, the first neural network (10 MLP 1-18-1) closest to zero came out as the best. This network should be further trained on more data in the future, to refine the results. [ABSTRACT FROM AUTHOR]

Accurate stock price prediction is very difficult in today's economy. Accurate prediction plays an important role in helping investors improve return on equity. As a result, a number of new approaches and technologies have logically evolved in recent years to predict stock prices. One is also the method of artificial neural networks, which have many advantages over conventional methods. The aim of this paper is to compare a method of exponential time series alignment and time series alignment using artificial neural networks as tools for predicting future stock price developments on the example of the company Unipetrol. Time series alignment is performed using artificial neural networks, exponential alignment of time series, and then a comparison of time series of predictions of future stock price trends predicted using the most successful neural network and price prediction calculated by exponential time series alignment is performed. Predictions for 62 business days were obtained. The realistic picture of further possible development is surprisingly given based on the exponential alignment of time series.

The present text focuses on perceiving real estate property as an investment asset that generates a certain amount of revenue to its owner, assuming expected risk and the expected level of liquidity. The first step is to determine the open market value of the selected property, which represent the expected expenses of the investment costs incurred (taking into account other acquisition costs), then we determine the open market rent value, which is the expected return on the selected property, then identify possible business risk associated with the commercial use of real estate and finally, the liquidity of the entire investment is estimated. In conclusion, methods for evaluating investments are applied to assess the realization of the investment – acquisition of the selected real estate for commercial purposes, the estimated return time and the percentage of the return on investment is calculated of the paper.

Artificial intelligence is largely used in many technical applications and allows you to provide various solutions in problem estimation, regression, or optimization. Artificial intelligence, specifically artificial neural networks, extend to the area of economics and finance. They are used primarily for operations that can´t be identified analytically. Neural networks are suitable for modelling very complex strategic decisions, for large sets of data, and so on. The main advantage is the ability to learn and then to capture hidden and strongly non-linear dependencies. In this paper they are used for the analysis of agricultural businesses. The aim is to analyse the state of the agricultural sector through the use of Kohonen networks and then to assess its future development. On the basis of the analysis, significant and large clusters of businesses are depicted, and the most significant clusters are analysed. It is possible to estimate the number of businesses that will be successful, those that will stagnate and those that will fail in the following period. Application of Kohonen networks is rather complex, but they have great potential and the results are very interesting.

AI provides the concept of machine learning that helps to automate the decision-making process by analyzing data inputs. This paper is focused on the usage of AI mechanisms in the Czech Republic across business segments. The data from the Czech statistical office provided from 6 794 companies on the usage of mechanism as text mining, speech recognition and generation, image recognition, and machine learning robotic process automatization. The most used mechanism is image recognition, which used all of the sectors and the least are speech generation and machine learning. [ABSTRACT FROM AUTHOR]

This paper concerns the capital structure of companies that apply the principles of circular economy. Such companies inquire information about this more sustainable approach. The objective of the paper is to find out what is the capital structure in companies that operate in the field of agriculture and are also actively involved in the circular economy system. The paper examines statistical characteristics and basic items of financial statements related to companies that apply and those that do not apply the principles of circular economy. The difference between these types of companies is thus sought. There is an analysis that includes data taken from the Cribis database of the CRIF - Czech Credit Bureau a.s. An abbreviated financial analysis focusing on absolute indicators of financial statements is used as well. [ABSTRACT FROM AUTHOR]