• Analytical Science and Technology
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• v.28 no.6
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• pp.398-408
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• 2015

Carbon monoxide (CO) intoxication, arising from CO from an ignited charcoal briquette (ICB), is a popular means of committing suicide in Korea. Most CO intoxications are related to suicide attempts; however, the possibility of a homicide disguised as a suicide cannot be ruled out. Therefore, forensic investigation of the deceased and the crime scene is crucial to confirm that the deceased committed suicide. Detection of the components of an ICB on the objects suspected of being contacted by the deceased, such as the hands, nostrils, and doorknobs, is essential for linking the crime scene to the victim in the case of suicides by ignited ICBs. The traces from an ICB were analyzed by investigating the morphological characteristics and obtaining elemental compositions. The ICBs were completely different from blackened wood, as detected by discriminant analysis with the elements of carbon and oxygen. We analyzed one case of CO intoxication to demonstrate an excellent procedure for verifying whether a suicide occurred with an ICB. We employed SEM-EDX for the analysis of an ICB, microscope-FT/IR and pyrolysis-GC/MS for a partly burnt resin-type substance, GC/MS for diphenhydramine (a sleeping drug), and GC/TCD for the CO-Hb level. We detected traces of an ICB on the hands, nostrils, and doorknobs, which were all discriminated into an ICB group. Detection of ICB traces from the nostrils could indicate that the deceased started the fire themselves to commit suicide. The partially burnt black material was analyzed as an acrylronitrilestyrene polymer, which is normally used to make bags for carrying or wrapping and could be assumed to have been used to transport the ICB. Diphenhydramine, a sleeping drug, was detected at a level of 2.3 mg/L in the blood, which was lower than that in fatal cases (8-31 mg/L; mean 16 mg/L). A CO-Hb level of 79% was found in the blood, which means that the cause of death was CO intoxication. The steps shown here could represent an ideal method for reaching a verdict of suicide by CO intoxication produced by burning an ICB in a sealed room or a car.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.