• Journal of Korean Society on Water Environment
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• v.40 no.3
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• pp.121-129
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• 2024

Harmful cyanobacterial blooms (HCBs) are caused by the rapid proliferation of cyanobacteria and are believed to be exacerbated by climate change. However, the extent to which HCBs will be stimulated in the future due to increased temperature remains uncertain. This study aims to predict the future occurrence of cyanobacteria in the Nakdong River, which has the highest incidence of HCBs in South Korea, based on temperature rise scenarios. Representative Concentration Pathways (RCPs) were used as the basis for these scenarios. Data-driven model simulations were conducted, and out of the four machine learning techniques tested (multiple linear regression, support vector regressor, decision tree, and random forest), the random forest model was selected for its relatively high prediction accuracy. The random forest model was used to predict the occurrence of cyanobacteria. The results of boxplot and time-series analyses showed that under the worst-case scenario (RCP8.5 (2100)), where temperature increases significantly, cyanobacterial abundance across all study areas was greatly stimulated. The study also found that the frequencies of HCB occurrences exceeding certain thresholds (100,000 and 1,000,000 cells/mL) increased under both the best-case scenario (RCP2.6 (2050)) and worst-case scenario (RCP8.5 (2100)). These findings suggest that the frequency of HCB occurrences surpassing a certain threshold level can serve as a useful diagnostic indicator of vulnerability to temperature increases caused by climate change. Additionally, this study highlights that water bodies currently susceptible to HCBs are likely to become even more vulnerable with climate change compared to those that are currently less susceptible.

• The KIPS Transactions:PartA
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• v.15A no.3
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• pp.141-149
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• 2008

In this research we present a novel method which combines and visualizes the design model and the FDM-based simulation result of solidification. Moreover we employ VR displays and visualize stereoscopic images to provide an effective analysis environment. First we reconstruct the solidification simulation result to a rectangular mesh model using a conventional simulation software. Then each point color of the reconstructed model represents a temperature value of its position. Next we map the two models by finding the nearest point of the reconstructed model for each point of the design model and then assign the point color of the design model as that of the reconstructed model. Before this mapping we apply mesh subdivision because the design model is composed of minimum number of points and that makes the point distribution of the design model not uniform compared with the reconstructed model. In this process the original shape is preserved in the manner that points are added to the mesh edge which length is longer than a predefined threshold value. The implemented system visualizes the solidification simulation data on the design model, which allows the user to understand the object geometry precisely. The immersive and realistic working environment constructed with use of VR display can support the user to discover the defect occurrence faster and more effectively.

• The Journal of Engineering Geology
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• v.26 no.2
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• pp.235-250
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• 2016

In Korea, 70% of the annual rainfall falls in summer, and the number of days of extreme rainfall (over 200 mm) is increasing over time. Because rainfall is the most important trigger of landslides, it is necessary to decide a rainfall threshold for landslide warning and to develop a landslide warning model. This study selected 12 study areas that contained landslides with exactly known triggering times and locations, and also rainfall data. The feasibility of applying a Rainfall Triggering Index (RTI) to Korea is analyzed, and three RTI models that consider different time units for rainfall intensity are compared. The analyses show that the 60-minute RTI model failed to predict landslides in three of the study areas, while both the 30- and 10-minute RTI models gave successful predictions for all of the study areas. Each RTI model showed different mean response times to landslide warning: 4.04 hours in the 60-minute RTI model, 6.08 hours in the 30-minute RTI model, and 9.15 hours in the 10-minute RTI model. Longer response times to landslides were possible using models that considered rainfall intensity for shorter periods of time. Considering the large variations in rainfall intensity that may occur within short periods in Korea, it is possible to increase the accuracy of prediction, and thereby improve the early warning of landslides, using a RTI model that considers rainfall intensity for periods of less than 1 hour.

• Journal of Korea Water Resources Association
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• v.37 no.5
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• pp.407-424
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• 2004

Using geomorphoclimatic unit hydrograph(GCUH), we estimated the fitness to calculate the mountainous area discharge and flash flood trigger rainfall(FFTR). First, we compared the GCUH peak discharge with the existing report using the design storm at the Dukcheon basin. Second, we compared the HEC-HMS(Hydrologic Engineering Center-Hydrologic Modeling System) model and GCUH with the observed discharge using the real rainfall events at the Taesu stage gage. Third, GCUH and NRCS(Natural Resources Conservation Service) were used for calculating FFTR and proper calculation method was shown. At the Dukcheon basin, the comparison result of using design storm was shown in Table 11, and it was not in excess of 1.1, except for the 30 year return period. In case of real rainfall events, the result was shown in Table 12, and GCUH discharges were all larger than the HEC-HMS model discharges, and they were very similar to the observed data at the Taesu stage gage. In this study, we found that GCUH was a very proper method in the calculation of mountainous discharge. At the Dukcheon basin, FFTR was 12.96 mm in the first 10 minutes when the threshold discharge was 95.59 $m^3$/sec.

• The Korean Journal of Pain
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• v.18 no.2
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• pp.107-112
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• 2005

Background: Nerve ligation injury may produce mechanical allodynia, but this can be reversed after an intrathecal administration of adenosine analogues. In many animal and human studies, ATP-sensitive potassium channel blockers have been known to reverse the antinociceptive effect of various drugs. This study was performed to evaluate the mechanical antiallodynic effects of spinal R-PIA (Adenosine A1 receptor agonist) and the reversal of these effects due to pretreatment with glibenclamide (ATP-sensitive potassium channel blocker). Thus, the relationship between the antiallodynic effects of R-PIA and ATP-sensitive potassium channel were investigated in a neuropathic model. Methods: Male Sprague Dawley rats were prepared by tightly ligating the left lumbar 5th and 6th spinal nerves and implantation of a chronic lumbar intrathecal catheter for drug administration. The mechanical allodynia was measured by applying von Frey filaments ipsilateral to the lesioned hind paw. And the thresholds for paw withdrawal assessed. In study 1, either R-PIA (0.5, 1 and $2{\mu}g$) or saline were administered intrathecally for the examination of the antiallodynic effect of R-PIA. In study 2, glibenclamide (2, 5, 10 and 20 nM) was administered intrathecally 5 min prior to an R-PIA injection for investigation of the reversal of the antiallodynic effects of R-PIA. Results: The antiallodynic effect of R-PIA was produced in a dose dependent manner. In study 1, the paw withdrawal threshold was significantly increased with $2{\mu}g$ R-PIA (P < 0.05). In study 2, the paw withdrawal threshold with $2{\mu}g$ R-PIA was significantly decreased almost dose dependently by intrathecal pretreatment of 5, 10 and 20 nM glibenclamide (P < 0.05). Conclusions: These results demonstrated that an intrathecal injection of ATP-sensitive potassium channel blockers prior to an intrathecal injection of adenosine A1 receptors agonist had an antagonistic effect on R-PIA induced antiallodynia. The results suggest that the mechanism of mechanical antiallodynia, as induced by an intrathecal injection of R-PIA, may involve the ATP-sensitive potassium channel at both the spinal and supraspinal level in a rat nerve ligation injury model.

• Journal of Ginseng Research
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• v.42 no.2
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• pp.183-191
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• 2018

Background: Ginseng saponin has long been used as a traditional Asian medicine and is known to be effective in treating various kinds of pain. Ginsenoside Rf is one of the biologically active saponins found in ginseng. We evaluated ginsenoside Rf's antinociceptive and anti-inflammatory effects, and its mechanism of action on adrenergic and serotonergic receptors, in an incisional pain model. Methods: Mechanical hyperalgesia was induced via plantar incision in rats followed by intraperitoneal administration of increasing doses of ginsenoside Rf (vehicle, 0.5 mg/kg, 1 mg/kg, 1.5 mg/kg, and 2 mg/kg). The antinociceptive effect was also compared in a Positive Control Group that received a ketorolac (30 mg/kg) injection, and the $Na{\ddot{i}}ve$ Group, which did not undergo incision. To evaluate the mechanism of action, rats were treated with prazosin (1 mg/kg), yohimbine (2 mg/kg), or ketanserin (1 mg/kg) prior to receiving ginsenoside Rf (1.5 mg/kg). The mechanical withdrawal threshold was measured using von Frey filaments at various time points before and after ginsenoside Rf administration. To evaluate the anti-inflammatory effect, serum interleukin $(IL)-1{\beta}$, IL-6, and tumor necrotizing $factor-{\alpha}$ levels were measured. Results: Ginsenoside Rf increased the mechanical withdrawal threshold significantly, with a curvilinear dose-response curve peaking at 1.5 mg/kg. $IL-1{\beta}$, IL-6, and tumor necrotizing $factor-{\alpha}$ levels significantly decreased after ginsenoside Rf treatment. Ginsenoside Rf's antinociceptive effect was reduced by yohimbine, but potentiated by prazosin and ketanserin. Conclusion: Intraperitoneal ginsenoside Rf has an antinociceptive effect peaking at a dose of 1.5 mg/kg. Anti-inflammatory effects were also detected.

• Korean journal of applied entomology
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• v.53 no.3
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• pp.311-315
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• 2014

Lantern fly(Lycorma delicatula) is a major invasive pest that causes withering symptom of agricultural crops by sucking tree sap and sooty mold symptom by producing honeydew. This study was conducted to investigate the occurrence pattern of lantern fly in grape orchards in Gyeonggi area and the effect of winter temperature on L. delicatula egg survival during 2010 to 2013. In Gyeonggi areas, overwintered L. delicatula eggs began to hatch from early May and nymphs peaked in mid May. Adults emerged from late July and laid eggs until early November. The survival of L. delicatula eggs during overwintering was largely affected by winter temperatures. The relationship between the number of days below a threshold temperature (x) in January and the survival rate of overwintering L. delicatula eggs (y) was using linear regression model. The best model selected by the lowest RSS (residual sum of square) between predicted and actual survival was y = -1.0486 x + 94.496 ($R^2=0.7067$) with $-11^{\circ}C$ of threshold temperature. These results should be helpful to conduct L. delicatula management programs, since the results provided relivable prediction for the winter survival of L. delicatula eggs and the phenology of egg hatch in the spring.

• Journal of Environmental Health Sciences
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• v.39 no.1
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• pp.90-98
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• 2013

Objectives: Many studies have shown that air pollution and temperature have adverse effects on mortality and morbidity. But the interactive effect between air pollution and temperature on mortality has been rarely investigated. This study aims to explore whether temperature modifies the associations between ambient particulate matter less than $10{\mu}m$ in diameter ($PM_{10}$) and mortality in Seoul, Korea. Methods: The time-series analysis examined the effect of the interaction between $PM_{10}$ and temperature on mortality from 1999 to 2010 in Seoul. In order to examine the interactive effect between $PM_{10}$ and temperature on mortality, we fitted a response surface model controlling the time-trends and meteorological variables. The effects of $PM_{10}$ were stratified by temperature stratum to quantitatively estimate the $PM_{10}$-health outcome associations. Results: When temperature was low (below the threshold temperature), the percentage increases per $10{\mu}g/m^3$ increase of $PM_{10}$ increased 0.38% (95% Confidence Interval[CI]: 0.09~0.68%) and 0.31% (95% CI: - 0.07~0.68%) of mortality in the all age group and ${\geq}65$ year age group, respectively. When temperature was high (above the threshold temperature), the percentage increases per $10{\mu}g/m^3$ increase of $PM_{10}$ increased 1.09% (95% CI: 0.47~1.72%) and 1.35% (95% CI: 0.65~2.06%) for mortality in the all age group and ${\geq}65$ year age group, respectively. Conclusion: The results of this study showed strong modification by temperature in the association between $PM_{10}$ and mortality. We recommend that public health strategies to minimize adverse health impact of heat and $PM_{10}$ should be considered in control and prevention measures for air pollution and weather-related health impacts.

• Journal of Intelligence and Information Systems
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• v.16 no.1
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• pp.71-92
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• 2010

As the use of trading systems increases recently, many researchers are interested in developing intelligent trading systems using artificial intelligence techniques. However, most prior studies on trading systems have common limitations. First, they just adopted several technical indicators based on stock indices as independent variables although there are a variety of variables that can be used as independent variables for predicting the market. In addition, most of them focus on developing a model that predicts the direction of the stock market indices rather than one that can generate trading signals for maximizing returns. Thus, in this study, we propose a novel intelligent trading system that mitigates these limitations. It is designed to use both the technical indicators and the other non-price variables on the market. Also, it adopts 'two-threshold mechanism' so that it can transform the outcome of the stock market prediction model based on support vector machines to the trading decision signals like buy, sell or hold. To validate the usefulness of the proposed system, we applied it to the real world data-the KOSPI200 index from May 2004 to December 2009. As a result, we found that the proposed system outperformed other comparative models from the perspective of 'rate of return'.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.35-47
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• 2022

Expansion of potato production areas can improve the food security in North Korea because the given crop has less requirements for agricultural materials and facilities. The Global Agro-Ecological Zones (GAEZ) model, which was developed to evaluate climate suitability under different cultivation conditions, was used to identify potential areas for the potato production. The spatial estimates of crop suitability under low and high input management conditions were downloaded from the GAEZ data portal. The values of suitability were obtained at the potato occurrence sites retrieved from the Global Biodiversity Information Facility (GBIF) database. The suitable areas for the potato production were identified using a threshold value derived from the suitability estimates at the occurrence sites. It was found that 90% of the occurrence sites had the suitability index value >3,333, which was set to be the threshold value. The suitable areas in North Korea were summarized by province and county. Rice cultivation areas were excluded from the analysis. The reported relative acreage of potato production was better represented by the suitable areas under the low input management options than the high input conditions. The suitable areas also had a similar distribution to the reported acreage of potato production by county. These results indicated that the GAEZ model would be useful to identify the candidate production areas, which would facilitate the increases in potato production especially under future climate conditions. Furthermore, monthly maps of crop suitability can be used to design cropping systems that would improve crop production under the limited use of agricultural materials and facilities.