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The topic of optimizing capital structure is very important for a company to work efficiently and reliably. It is important for every company to optimize everything so that they have the highest possible efficiency. Entrepreneurs also try to make this optimization last them as long as possible. Therefore, the aim of this paper is to determine the optimal capital structure of an agricultural company operating in the Czech Republic. The base source of data is the closing data of individual agricultural companies from the Albertina database of Bisnode. More than 9,000 agricultural enterprises operating in the given sector of the national economy in the Czech Republic are recorded in this data set. The calculation of the WACC method and the level of debt of individual agricultural companies are used. Subsequently, the equation for calculating the cost of capital is determined using power regression. Here we also obtain the value of reliability, which in this particular case is not ideal, but still reasonable. A line chart is used to determine the optimal interval for the agricultural company. The optimal debt interval comes out to 20 to 25%, at which the cost of capital is declared to be from 22 to 24%. If agricultural companies had higher or lower debt ratios, it would be inefficient for the enterprise.

The issue of business risk in times of recession or growth is very topical in these times. The Czech Republic and its neighboring countries are currently struggling with the economic problems caused by the Covid-19 pandemic. The aim of the paper is to analyze the number of insolvency petitions in the Czech Republic from the years 2010–2020 and to compare them with each other. The data source are data from publicly available resources from the Creditreform group and also from the CRIF database of the Cribis platform. First were created tables for the Agriculture and Forestry, Manufacturing, Construction and Transport sectors. Data for the relevant years were added to them from the aforementioned resources. Then was created a line chart for each industry. Using time series analysis and comparison, we analyze and compare the development of insolvencies. At the same time, we use causal analysis to find out why there were high numbers of insolvencies in the given years. We are also looking at which sectors were hit hardest during the insolvency crisis, when they were hit hardest, how they did in times of economic growth and how they should adapt to the next possible crisis. During the Great Recession, the construction sector was hit the hardest, and then the transport sector also suffered greatly. The largest numbers of insolvencies can be observed in 2012. At a time of economic growth, which began in 2014, the numbers of insolvencies fell in all sectors analyzed, but mostly in agriculture and forestry and transport. Businesses in the sectors analyzed can adapt to the next crisis by creating larger cash reserves or changing, for example, crop production or transport by temporarily reducing fares to attract new customers. We see the benefit of this work in the analysis of the number of insolvencies in the given sectors, which has not been recorded in almost any academic papers. We also see a benefit in determining the conditions for how companies can prevent another crisis.

BACKGROUND The citation trend of research has frequently been applied to topical entities of interest in bibliographical studies. Although the burst spot and the corresponding burst strength can be highlighted in the traditional temporal bar graph (TBG) as viewing their evolutions of entities, no such growth trends were added to the TBG for a better understanding of future tremd (e.g., reading to rise, increasing, decreasing, or slowdown). A step-by-step scheme for constructing the bibliographical study is thus required to make data meaningful and fruitful. OBJECTIVE This article assesses (1) whether an EISTL model (i.e., identifying the topical entity, indicator, selection of top entities, TBG, and using the line-chart plot for verification) can be applied to display the trend analysis of article citations for entities and (2) whether the TBG can be enhanced to release more valuable information for readers. METHODS We obtained 2,151 abstracts indexed in PubMed by searching the keywords “JMIR mHealth and uHealth” (Journal) on November 11, 2021. The metadata was collected, including author names, research institutes, article identifiers (PMIDs), countries, and medical subject headings (MeSH terms). The burst spot and the growth trend were displayed along with the inflection point (IP) using the Newton–Raphson Iteration Method (NRIM) and the growth/share matrix (GSM). Cooccurrence analysis was performed to select the top-cited entities using social network analysis (SNA) and Sankey diagrams. The TBG plays a transitive role before drawing the line-chart plot in the EISTL model. Both choropleth map and Kano diagram were used to compare and classify research achievements (RA) for countries using the x-index. The differences in RAs were compared between two groups (i.e., participants of Summit for Democracy (SFD) 2021 and Non-SFD) using the forest plot. All animation-typed dashboards were laid on Google Maps for readers to manipulate entities of interest on their own. RESULTS The burst spot and citation trend for the top entities were selected and displayed on the TBG. The most cited entities were sequentially shown in the Sankey diagram, including Stoyan R Stoyanov (Australia), Queensland University of Technology (Australia), PMID=25760773, the US, and standards (MeSH). The top three most cited counties/regions were highlighted in a choropleth map and Kano diagram using the x-index to stratify in descending order: Australia, the UK, and Canada with x-indexes of 23.26, 22.21, and 21.42, respectively, when the US and China were divided into individual states and provinces for comparison. Differences in the six selective bibliometric metrics were not found (p>0.05) in countries between SFDs and non-SFDs. CONCLUSIONS We verified that (1) the EISTL model is viable and useful for presenting citation trends in bibliometric research, and (2) the improved TBG mode releases more information about citation trends. The EISTL model makes the bibliometrics clearer and easier to understand. As a corollary, the TBG with citation trends and burst spots is recommended for future bibliometrics and is not merely limited to the citation trends of the JMIR mHealth and uHealth, as we did in this study.

Student evaluation in the new era should be rooted in the educational macro background of "cultivating people by virtue" and comply with the overall policy orientation of educational evaluation reform. This research focuses on the construction and implementation of value-added evaluation system. Combined with the characteristics of vocational school students, this paper constructs a four-level index system and refines the student evaluation standards. The evaluation subjects are students, head teachers, teachers, parents and enterprise managers. Through the development of the related APP, it is convenient for each evaluation subject to input information, and finally use K-line chart to visually present the change process of the appreciation in students' literacy. A value-added evaluation system with strong timeliness, objectivity, motivation and operability is constructed.

This study aims to develop a model of dashboard information system to improve the quality of scientific journal management. The method used to develop the model of the dashboard is prototyping. The dashboard was also developed using the Pivot Table, Histogram/ Column Chart, Line Plot/ Line Chart, and Area Chart approach. For case study, the information displayed in the dashboard which from data recorded in the MAVIB Journal. The information used includes information about the score of articles that have been received, website visitor in the form of a summary report View Page Statcounter. It can be downloaded in Excel form. The Prototype Dashboard Information System and the addition of Additional Menu in OJS were made using figma, so that it becomes an illustration for designing and updating the appearance of OJS website pages. The information displayed in the dashboard is used to make decisions in order to improve the management of scientific journals. The results show that the dashboard model successfully displays strategic information, tactical information and transactional information. This information can be used by journal managers to make decisions in improving the quality of journal management.

The phrase “flatten the curve” has recently come into the vernacular to encourage slowing the spread of COVID-19. The concept comes from the Centre for Disease Control (CDC) report (p.18), where the curve renders the daily number of COVID-19 infections on the Y-axis and the day these are counted on X-axis of a line chart. A flatter curve visually depicts when the illness is under control, while a higher peak curve emerges when the health case system becomes overwhelmed and instances of the virus are high. Together we work to “flatten the curve” by following the recommendations of our Chief Medical Officers of Health.This is not the only visual metaphor used to refer to COVID-19, however; the “second wave” has also been used to describe the spread of COVID-19. This term refers to another peak in the line chart that may appear as restrictions are lifted and cases spike as a result. By visualizing health data, we can see the spread of COVID-19 and set public health goals through the peaks and valleys of a line chart.

Visual understanding of the implied knowledge in line charts is an important task affecting many downstream tasks in information retrieval. Despite common use, clearly defining the knowledge is difficult because of ambiguity, so most methods used in research implicitly learn the knowledge. When building a deep neural network, the integrated approach hides the properties of individual subtasks, which can hinder finding the optimal configurations for the understanding task in academia. In this paper, we propose a problem definition for explicitly understanding knowledge in a line chart and provide an algorithm for generating supervised data that are easy to share and scale-up. To introduce the properties of the definition and data, we set well-known and modified convolutional neural networks and evaluate their performance on real and synthetic datasets for qualitative and quantitative analyses. In the results, the knowledge is explicitly extracted and the generated synthetic data show patterns similar to human-labeled data. This work is expected to provide a separate and scalable environment to enhance research into technical document understanding.

The advent of Coronavirus-19 (COVID-19) has created a new threat in terms of economy and life. Frankly, the adverse effects of COVID-19 can put our life at risk if we are contaminated. The recent promising cases in Malaysia have certainly intensified from day to day, going from bad to worse. The primary factor that causes the raising problem of COVID cases is the absence of cooperation between Malaysian and government. This research aims at visualizing the current situation of COVID-19 and thus raising consciousness among Malaysians to solve this dilemma. To fulfill the objectives, there are two stages of processes need to be performed. Using the dataset from Internet, the first section would use Microsoft Excel to create visualization tools such as a pie chart and a line chart. Next, the second part will scrap the Twitter data to explore how Malaysians are aware of COVID by using “Twint” function in Python software. The finding reveals that current COVID situation in Malaysia is in a severe stage since the chart shows that it has an exponential growth.  Moreover, the Twitter activity has indicated that the people are not paying attention to the COVID topic shared by Malaysia Ministry of Healthy (MOH) Consequently, the new positive cases increase dramatically after September 2020 in Malaysia. In conclusion, the people are more concern to the COVID news from MOH during the implementation of MCO and CMCO. The people lose concern when the number of cases dropped or the MCO and CMCO is ended.

Abstract To analyze SARS CoV-2 genomes, this paper uses a viral genome collected in Wuhan as a basic sequence to select a set of genomes from G20 countries/regions for comparison. Two methods are applied in visualization followed in the A1 and C1 modules of the MAS to provide 1D similarity projection and information entropy approaches. For a pair of two genomes segmented as M segments to calculate base differences corresponding to each segment, a measuring vector can be generated to be projected as a visual map. Refined information will be provided to calculate the information entropy corresponding to base differences. This provides quantitative measures to organize these genomes arranged into a linear order. The first method uses a line chart representation to illustrate relevant differences among genomes 1 by 1 between Wuhan and other regions. The second method uses a histogram representation to observe the information entropy projection results. Significant similarities are observed among the differences with approximately equal entropies with higher similarity. Based on the comparison of gene sequence diagrams between Wuhan and various regions, a difference analysis was carried out.