The aim of this research is to construct an SIR model for COVID-19 with fuzzy parameters. The SIR model is constructed by considering the factors of vaccination, treatment, obedience in implementing health protocols, and the corona virus-load. Parameters of the infection rate, recovery rate, and death rate due to COVID-19 are constructed as a fuzzy number, and their membership functions are used in the model as fuzzy parameters. The model analysis uses the generation matrix method to obtain the basic reproduction number and the stability of the model’s equilibrium points. Simulation results show that differences in corona virus-loads will also cause differences in the transmission of COVID-19. Likewise, the factors of vaccination and obedience in implementing health protocols have the same effect in slowing or stopping the transmission of COVID-19 in Indonesia.
The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. In the simulation data is obtained that the data contained AO initial models are ARIMA (2,0,0) with MSE = 36,780, after the detection and correction of data obtained by the iteration of the model ARIMA (2,0,0) with the coefficients obtained from the regression 1 2 1 2 3 0,106 0, 204 0, 401 329 115 35,9 t t t Z Z Z X t X t X t and MSE = 19,365. This shows that there is an improvement of forecasting error rate data.
Dataset compiled from spreading hot spots, responsible for fire risk in many regions of Indonesian forests, are complex, primarily induced by the large size of the observed regions and high variation of hot spot distribution. The challenge in analyzing this type of dataset is to develop statistical techniques that facilitate the analysis, visualization, and interpretation of the results. Techniques, such as multivariate analysis and artificial neural networks, have been applied to resolve the highdimensional space in such large datasets. Each method uses a different rationale for how the relationship between the input parameters will be preserved during analysis. This study presents the use of a principal component analysis (PCA) and a selforganizing map (SOM) to reduce the high dimensionality of the input variables and, subsequently to visualize the dataset into a two-dimensional (2-D) space. The results indicate that the first two principal components of the PCA provide a large percentage of cumulative variance to explain the data patterns. However, a comparison of the data projection, SOM is better suited than PCA in visualizing the fire-risk distribution in forests. The SOM color-coding and labeling also effectively visualized a classification system of fire risk via node clusters, in such a way that the fire risks level according to their hot spot locations in forest is easily interpreted.
Two new naphtoquinone derivatives, 8-hydroxy-astropaquinone B (1) and astropaquinone D (2) was isolated from Fusarium napiforme, endophyte fungus isolated from a mangrove plant, Rhizophora Mucronata together with a known compound, 3-O-methyl-9-O-methylfusarubin (3). The structures of 1 and 2 were determined by spectroscopic methods, mainly by 2D NMR spectroscopic analyses. Compounds 1, 2 and 3 exhibited antimicrobial activities and phytotoxicities.
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