We investigate the problem of mathematical modeling of new corona virus (COVID-19) in Poland and tries to predict the upcoming wave. A Gaussian mixture model is proposed to characterize the COVID-19 disease and to predict a new / future wave of COVID-19. This prediction is very much needed to prepare for medical setup and continue with the upcoming program. Specifically, data related to the new confirmed cases of COVID-19 per day are considered, and then we attempt to predict the data and statistical activity. A close match between actual data and analytical data by using the Gaussian mixture model shows that it is a suitable model to present new cases of COVID-19. In addition, it is thought that there are N waves of COVID-19 and that information for each future wave is also present in current and previous waves as well. Using this concept, predictions of a future wave can be made.
Highlights
Four phases can be distinguished in the spread of the virus.
The relative daily change indicator best visualizes the changes in the epidemic.
We present the changes in the epidemic phases for a number of selected countries.
The re-outbreak phase is probably the expected second wave of the epidemic.
An updated, well-commented Matlab file is available in the open GitHub repository.
Knowledge discovery is an important aspect of human cognition. The advantage of the visual approach is in opportunity to substitute some complex cognitive tasks by easier perceptual tasks. However for cognitive tasks such as financial investment decision making this opportunity faces the challenge that financial data are abstract multidimensional and multivariate, i.e., outside of traditional visual perception in 2D or 3D world. This paper presents an approach to find an investment strategy based on pattern discovery in multidimensional space of specifically prepared time series. Visualization based on the lossless Collocated Paired Coordinates (CPC) plays an important role in this approach for building the criteria in the multidimensional space for finding an efficient investment strategy. Criteria generated with the CPC approach allow reducing/compressing space using simple directed graphs with beginnings and the ends located in different time points. The dedicated subspaces constructed for time series include characteristics such as Bollinger Band, difference between moving averages, changes in volume etc. Extensive simulation studies have been performed in learning/testing context. Effective relations were found for one-hour EURUSD pair for recent and historical data. Also the method has been explored for one-day EURUSD time series n 2D and 3D visualization spaces. The main positive result is finding the effective split of a normalized 3D space on 4x4x4 cubes in the visualization space that leads to a profitable investment decision (long, short position or nothing). The strategy is ready for implementation in algotrading mode.
The aim of the article is to determine in the studied groups the multiple intelligence distribution defined in the 1980s by Howard Gardner. The research was conducted in three groups of respondents. The first study group was first-year students of computer science, the second was master (2nd degree) students, educationally 4 years older than the first group. Their intelligence distributions were compared with the intelligence distributions of the third group-graduates of the same university, the same field of study after several years of work in positions consistent with their education. Participants filled one of the multiple intelligence tests selected by answering 24 questions. A group of approximately 110 students and approximately 40 IT employees were examined. As there were statistically justified differences in several significant sub intelligences, a discussion was held on the forms of educational impact on student development paths. The research was carried out in conditions of full voluntary participation in the test and on the basis of self-assessment according to questions suggested in one of the online sources. According to the authors, the results seem interesting, although surprising.
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