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]

Research background: Connected Internet of Robotic Things (IoRT) and cyber-physical process monitoring systems, industrial big data and real-time event analytics, and machine and deep learning algorithms articulate digital twin smart factories in relation to deep learningassisted smart process planning, Internet of Things (IoT)-based real-time production logistics, and enterprise resource coordination. Robotic cooperative behaviors and 3D assembly operations in collaborative industrial environments require ambient environment monitoring and geospatial simulation tools, computer vision and spatial mapping algorithms, and generative artificial intelligence (AI) planning software. Flexible industrial and cloud computing environments necessitate sensing and actuation capabilities, cognitive data visualization and sensor fusion tools, and image recognition and computer vision technologies so as to lead to tangible business outcomes. Purpose of the article: We show that generative AI and cyber--physical manufacturing systems, fog and edge computing tools, and task scheduling and computer vision algorithms are instrumental in the interactive economics of industrial metaverse. Generative AI-based digital twin industrial metaverse develops on IoRT and production management systems, multisensory extended reality and simulation modeling technologies, and machine and deep learning algorithms for big data-driven decision-making and image recognition processes. Virtual simulation modeling and deep reinforcement learning tools, autonomous manufacturing and virtual equipment systems, and deep learning-based object detection and spatial computing technologies can be leveraged in networked immersive environments for industrial big data processing. Methods: Evidence appraisal checklists and citation management software deployed for justifying inclusion or exclusion reasons and data collection and analysis comprise: Abstrackr, Colandr, Covidence, EPPI Reviewer, JBI-SUMARI, Rayyan, RobotReviewer, SR Accelerator, and Systematic Review Toolbox. Findings & value added: Modal actuators and sensors, robot trajectory planning and computational intelligence tools, and generative AI and cyber--physical manufacturing systems enable scalable data computation processes in smart virtual environments. Ambient intelligence and remote big data management tools, cloud-based robotic cooperation and industrial cyber-physical systems, and environment mapping and spatial computing algorithms improve IoT-based real-time production logistics and cooperative multi-agent controls in smart networked factories. Context recognition and data acquisition tools, generative AI and cyber--physical manufacturing systems, and deep and machine learning algorithms shape smart factories in relation to virtual path lines, collision-free motion planning, and coordinated and unpredictable smart manufacturing and robotic perception tasks, increasing economic performance. This collective writing cumulates and debates upon the most recent and relevant literature on cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative AI and cyber--physical manufacturing systems in the immersive industrial metaverse by use of evidence appraisal checklists and citation management software. [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.