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Fog, freezing rain and snow (melt) quickly condense on road surfaces, forming black ice that is difficult to identify and causes major accidents on highways. As a countermeasure to prevent icing car accidents, it is necessary to predict the amount and location of black ice. This study advanced previous models through machine learning and multi-sensor verified results. Using spatial (hill shade, slope, river system, bridge, and highway) and meteorological (air temperature, cloudiness, vapour pressure, wind speed, precipitation, snow cover, specific heat, latent heat, and solar radiation energy) data from the study area (Suncheon-Wanju Highway in Gurye-gun, Jeollanam-do, South Korea), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with Geo-Information System in units of 1 m2. The intermediate factors calculated as input factors were road temperature and road moisture, modelled using a deep neural network (DNN) and numerical methods. Considering the results of the DNN, the root mean square error was improved by 148.6% and reliability by 11.43% compared to a previous study (Linear Regression). Based on the model results, multiple sensors were buried at four selected points in the study area. The model was compared with sensor data and verified with the upper-tailed test (with a significance level of 0.05) and Fast Fourier Transform (freezing does not occur when wavelength = 0.00001 Hz). Results of the verified simulation can provide valuable data for government agencies like road traffic authorities to prevent traffic accidents caused by black ice. [ABSTRACT FROM AUTHOR]

The article presents a study which characterizes 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity in red pepper that is grown at Jinan, South Korea. The study is conducted through the isolation of the ACC deaminase activity of a Pseudomonas entomophilia strain PS-PJH from the rhizosphere region of the red pepper. The results show less than 99 identity between nucleotide and amino acid with acdS genes.

In an effort to identify the chemical constituents of fruiting bodies of Fomitopsis pinicola, a new lanostane triterpene glycoside, designated as fomitoside K, has been isolated from its methanolic extract. Its chemical structure was assigned on the basis of various spectroscopic studies. [ABSTRACT FROM AUTHOR]