• Sleep Medicine and Psychophysiology
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• v.17 no.1
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• pp.41-49
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• 2010

Unlike the case of adult obstructive sleep apnea syndrome (OSAS), there was no consistent finding on the changes of sleep architecture in childhood OSAS. Further understanding of the sleep electroencephalogram (EEG) should be needed. Non-linear analysis of EEG is particularly useful in giving us a new perspective and in understanding the brain system. The objective of the current study is to compare the sleep architecture and the scaling exponent (${\alpha}$) from detrended fluctuation analysis (DFA) on sleep EEG between OSAS and normal children. Fifteen normal children (8 boys/7 girls, 6.0${\pm}4.3$2.2 years old) and twelve OSAS children (10 boys/2 girls, 6.4${\pm}4.3$3.4 years old) were studied with polysomnography (PSG). Sleep-related variables and OSAS severity indices were obtained. Scaling exponent of DFA were calculated from the EEG channels (C3/A2, C4/A1, O1/A2, and O2/A1), and compared between normal and OSAS children. No difference in sleep architecture was found between OSAS and normal controls except stage 1 sleep (%) and REM sleep latency (min). Stage 1 sleep (%) was significantly higher and REM latency was longer in OSAS group (9.3${\pm}4.3$4.3%, 181.5${\pm}4.3$59.9 min) than in controls (5.6${\pm}4.3$2.8%, 133.5${\pm}4.3$42.0 min). Scaling exponent (${\alpha}$) showed that sleep EEG of OSAS children also followed the 'longrange temporal correlation' characteristics. Value of ${\alpha}$ increased as sleep stages increased from stage 1 to stage 4. Value of ${\alpha}$ from C3/A2, C4/A1, O1/A2, O2/A1 were significantly lower in OSAS than in control (1.36${\pm}4.3$0.05 vs. 1.41${\pm}4.3$0.04, 1.37${\pm}4.3$0.04 vs. 1.41${\pm}4.3$0.04, 1.37${\pm}4.3$0.05 vs. 1.41${\pm}4.3$0.05, and 1.36${\pm}4.3$0.07 vs. 1.41${\pm}4.3$0.05, p<0.05). Higher stage 1 sleep (%) in OSAS children was consistent finding with OSAS adults. Lower $'{\alpha}'$ in OSAS children suggests decrease of self-organized criticality or the decreased piling-up energy of brain system during sleep in OSAS children.

• The Korean Journal of Pesticide Science
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• v.14 no.2
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• pp.148-156
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• 2010

S59-4 isolate was evaluated as a potential biocontrol agent using in vivo wounded garlic bulb assay. When the spore suspension ($10^5$ spores/$m\ell$) of Penicillium hirsutum was co-inoculated with cell suspension of S59-4 isolate on wounded garlics, the isolate showed high suppressive effect to disease development. The isolate was identified as Pantoea agglomerans S59-4(Pa59-4) through Biolog system. Furthermore, soaking garlic bulbs in the suspension of Pa59-4 significantly reduced garlic decay caused by P. hirsutum. The optimal concentration of Pa59-4 for controlling garlic blue mold was $10^7\sim10^8$ cfu/$m\ell$. And suppressive effect of Pa59-4 on garlic storage decay reduced as inoculation concentration of Penicillium hirsutum increased. In addition in order to investigate population dynamics of Pa59-4 on application site of garlic cloves, two antibiotic markers, pimaricin and vancomycin were selected. Bacterial density of Pa59-4 on the wounded garlic cloves increased continuously both under room temperature condition and low temperature condition until 30days after application of Pa59-4, meanwhile that of Pa59-4 on intact garlic cloves increased until 15days after application of Pa59-4 and thereafter decreased continuously. Two culture media for mass-production of Pa59-4, LB medium and TSB medium, were selected. By-product of bio-fungicide formulated by mixing white carbon and bacterial suspension of Pa59-4 suppressed by 40 to 50% garlic blue mold. Above results suggest that Pa59-4 be a promising control agent against garlic blue mold.

• Journal of Korean Society of Forest Science
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• v.109 no.3
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• pp.271-280
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• 2020

This study compared soil CO₂ efflux rates after fertilization, in Pinus densiflora and Quercus variabilis stands. Compound fertilizers were applied to the forest floor in March 2016, following a one-year calibration period (from March 2015 to February 2016). In situ soil CO₂ efflux rates were measured every month during the two-year study periods, using an infrared gas analyzer with a closed chamber system. Mean annual soil CO₂ efflux rates were higher following fertilizer application in the P. densiflora and Q. variabilis stands (P. densiflora: 2.180 μmol m^-2 s^-1; Q. variabilis: 1.977 μmol m^-2 s^-1) as compared with the rates measured during the calibration period (P. densiflora: 1.620 μmol m^-2 s^-1; Q. variabilis: 1.557 μmol m^-2 s^-1). The mean annual soil CO₂ efflux rates in the unfertilized treatments of both stands were not significantly different between the two-year study periods. The Q₁₀ values of fertilized treatments in Q. variabilis stands were higher in the fertilization period (3.41) than in the calibration period (3.14), whereas the Q₁₀ values in P. densiflora stands did not change between the fertilization and calibration periods. The Q₁₀ values of unfertilized treatments in the Q. variabilis stands were lower during the 2016-2017 period (3.69), than in the 2015-2016 period (3.85), whereas the Q₁₀ values in P. densiflora stands were higher during the 2016-2017 period (3.65), than in the 2015-2016 period (3.15). These results indicate that the increase in soil CO₂ efflux rates in P. densiflora stands could be more sensitive to fertilization compared with the rates in Q. variabilis stands.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.