• Korean Journal of Agricultural and Forest Meteorology
• /
• v.10 no.4
• /
• pp.158-166
• /
• 2008

The pumpkin fruit fly, Bactrocera depressa (Tephritidae: Diptera), is one of the most important pests in Cucurbitaceae plants. This study was conducted to investigate the basic ecology of B. depressa, and to develop a forecasting model for predicting the time of adult emergence in early season. In green pumpkin producing farms, the oviposition punctures caused by the oviposition of B. depressa occurred first between mid- and late July, peaked in late August, and then decreased in mid-September followed by disappearance of the symptoms in late September, during which oviposition activity of B. depressa is considered active. In full-ripened pumpkin producing farms, damaged fruits abruptly increased from early Auguest, because the decay of pumpkins caused by larval development began from that time. B. depressa produced a mean oviposition puncture of 2.2 per fruit and total 28.8-29.8 eggs per fruit. Adult emergence from overwintering pupae, which was monitored using a ground emergence trap, was first observed between mid- and late May, and peaked during late May to early June. The development times from overwintering pupae to adult emergence decreased with increasing temperature: 59.0 days at $15^{\circ}C$, 39.3 days at $20^{\circ}C$, 25.8 days at$25^{\circ}C$ and 21.4 days at $30^{\circ}C$. The pupae did not develop to adult at $35^{\circ}C$. The lower developmental threshold temperature was calculated as $6.8^{\circ}C$ by linear regression. The thermal constant was 482.3 degree-days. The non-linear model of Gaussian equation well explained the relationship between the development rate and temperature. The Weibull function provided a good fit for the distribution of development times of overwintering pupae. The predicted date of 50% adult emergence by a degree-day model showed one day deviation from the observed actual date. Also, the output estimated by rate summation model, which was consisted of the developmental model and the Weibull function, well pursued the actual pattern of cumulative frequency curve of B. depressa adult emergence. Consequently, it is expected that the present results could be used to establish the management strategy of B. depressa.

• Neonatal Medicine
• /
• v.18 no.1
• /
• pp.49-58
• /
• 2011

Purpose: The purpose of this study was to evaluate the relationship between cord blood adiponectin and insulin-like growth factor (IGF)-I and their effect on fetal growth and insulin resistance in mothers with gestational diabetes mellitus (GDM). Methods: Cord blood adiponectin and IGF-I were compared between mothers with GDM (GDM group, N=53) and controls (non-GDM group, N=101). Neonates were classified into three groups of small for gestational age (SGA, N=26), appropriate for gestational age (AGA, N=97), and large for gestational age (LGA, N=31) by birth weight. The association between cord adiponectin and IGF-I levels was evaluated in relation to maternal and neonatal clinical data. Results: Cord adiponectin was lower in the GDM group than in the non-GDM group (P<0.001). There was no significant difference in cord adiponectin among the SGA, AGA, and LGA groups in the GDM group (P=0.228). The cord adiponectin of AGA in the GDM group was significantly lower than that in the non-GDM group (P<0.001). The most powerful predictor affecting cord adiponectin was the result of maternal 75 g oral glucose tolerance test. The cord IGF-I values between the GDM group and the non-GDM group were not different (P=0.834). Neonates with the heavier birth weight had the higher cord IGF-I levels. The most powerful predictor affecting cord IGF-I was birth weight and the next was maternal parity. Conclusion: Both cord blood adiponectin and IGF-I were associated with fetal growth, but IGF-I was a more general and direct factor affecting fetal body size, and adiponectin seemed to have more association with insulin sensitivity than growth.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2003.05a
• /
• pp.181-186
• /
• 2003

The satellite navigation system is widely used for identifying a user's position regardless of weather or geographic conditions and also make effect on new technology of marine LBS(Location Based Service), which has the technology of geographic information such as the ENC. Generally, there are conceivable systems of marine LBS such as ECDIS, or ECS that use the ENC itself with powerful processor in installed type on ships bridge. Since the ENC is relatively heavy structure with dummy format for data transfer between different systems, we should reduce the ENC to small and compact size in order to use it in mobile platform. In this paper, we assumed that the mobile system like PDA, or Webpad can be used for small capability of mobile platform. However, the ENC should be updated periodically by update profile data produced by HO. If we would reduce the ENC without a consideration of update, we could not get newly updated data furthermore. As summary, we studied considerations for ENC reduction with update capability. It will make the ENC be useful in many mobile platforms for various applications.

• Journal of Korea Port Economic Association
• /
• v.36 no.4
• /
• pp.17-30
• /
• 2020

In the shipping industry, it is essential to engage in the preemptive prediction of freight rate volatility through market monitoring. Considering that freight rates have already started to fall, the loss of shipping companies will soon be uncontrollable. Therefore, in this study, factors affecting the freight rates of bulk carriers, which have relatively large freight rate volatility as compared to container freight rates, were quantified and analyzed. In doing so, we intended to contribute to future shipping market monitoring. We performed an analysis using a vector error correction model and estimated the influence of six independent variables on the charter rates of bulk carriers by Handy Size, Supramax, Panamax, and Cape Size. The six independent variables included the bulk carrier fleet volume, iron ore traffic volume, ribo interest rate, bunker oil price, and Euro-Dollar exchange rate. The dependent variables were handy size (32,000 DWT) spot charter rates, Supramax 6 T/C average charter rates, Pana Max (75,000 DWT) spot charter, and Cape Size (170,000 DWT) spot charter. The study examined charter rates by size of bulk carriers, which was different from studies on existing specific types of ships or fares in oil tankers and chemical carriers other than bulk carriers. Findings revealed that influencing factors differed for each ship size. The Libo interest rate had a significant effect on all four ship types, and the iron ore traffic volume had a significant effect on three ship types. The Ribo rate showed a negative (-) relationship with Handy Size, Supramax, Panamax, and Cape Size. Iron ore traffic influenced three types of linearity, except for Panamax. The size of shipping companies differed depending on their characteristics. These findings are expected to contribute to the establishment of a management strategy for shipping companies by analyzing the factors influencing changes in the freight rates of charterers, which have a profound effect on the management performance of shipping companies.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_1
• /
• pp.627-646
• /
• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.155-175
• /
• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of the Korean Society of Urban Environment
• /
• v.18 no.4
• /
• pp.473-483
• /
• 2018

In this study, we have developed a lab scale bioreactor to identify the characteristics of nitrification reaction according to operation condition (temperature, inhibitor (as Cl), activated nitrifying bacteria (ANB). etc) to improve nitrification efficiency at low temperature. Recovery rate of nitrification took about 4 days to reach the normal level by injected ANB after inhibition shock of CI injection at $20^{\circ}C$, when measured the concentration of $NO_2{^-}-N+NO_3{^-}-N$ in the effluent. In the case of $10^{\circ}C$, recovery of nitrification rate took about 4 days to reach the level of half to the normal level and 7 days for complete recovery which took 3 days more than those at $20^{\circ}C$. At $10^{\circ}C$ considering the winter season, the specific nitrification rate(SNR) of the from 1 day to 6 days after injected ANB according to its operation condition increased from 0.029 to 0.767 mgN/gSS/hr. The simulated SNR for the 8th day after the injected ANB at $10^{\circ}C$ was 0.840, 3.625 mgN/gSS/hr, respectively as linear function and exponential function, expecting to exceed level of 2.592 mgN/gSS/hr at normal condition. It was confirmed that injection of ANB during low temperature operation has many effects for improving nitrification efficiency through this study. In future studies, if further studies are carried out the determination of ANB injection and the design of efficient ANB reactor considering the changes of operating characteristics by site, it will contribute to the improvement of nitrification efficiency in winter season.

• Journal of Korean Society of Transportation
• /
• v.20 no.2
• /
• pp.105-115
• /
• 2002

For many rears, traffic accident statistics are the most direct measure of safety for a signalized intersection. However it takes more than 2 or 3 yearn to collect certain accident data for adequate sample sizes. And the accident data itself is unreliable because of the difference between accident data recorded and accident that is actually occurred. Therefore, it is rather difficult to evaluate safety for a intersection by using accident data. For these reasons, traffic conflict technique(TCT) was developed as a buick and accurate counter-measure of safety for a intersection. However, the collected conflict data is not always reliable because there is absence of clear criteria for conflict. This study developed objective and accurate conflict criteria, which is shown below based on traffic engineering theory. Frist, the rear-end conflict is regarded, when the following vehicle takes evasive maneuver against the first vehicle within a certain distance, according to car-following theory. Second, lane-change conflict is regarded when the following vehicle takes evasive maneuver against first vehicle which is changing its lane within the minimum stopping distance of the following vehicle. Third, cross and opposing-left turn conflicts are regarded when the vehicle which receives green sign takes evasive maneuver against the vehicle which lost its right-of-way crossing a intersection. As a result of correlation analysis between conflict and accident, it is verified that the suggested conflict criteria in this study ave applicable. And it is proven that estimating safety evaluation for a intersection with conflict data is possible, according to the regression analysis preformed between accident and conflict, EPDO accident and conflict. Adopting the conflict criteria suggested in this study would be both quick and accurate method for diagnosing safety and operational deficiencies and for evaluation improvements at intersections. Further research is required to refine the suggested conflict criteria to extend its application. In addition, it is necessary to develope other types of conflict criteria, not included in this study, in later study.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.1
• /
• pp.29-41
• /
• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Sleep Medicine and Psychophysiology
• /
• v.19 no.1
• /
• pp.27-34
• /
• 2012

Objectives: The analysis of heart rate variability (HRV) is a useful non-invasive tool to investigate the autonomic nerve function. Previous studies on the relationship between HRV and depression have been reported controversial results. Similarly, the correlation between the serum lipids and depression is debatable. The purpose of this study is to examine the relationship between heart rate variability, lipid profile and depression. Methods: A total of 42 patients with major depressive disorder (MDD) and 32 age and sex-matched normal subjects who had no previous history of major medical and mental illnesses were recruited for this study. A structured-interview was used to assess the general characteristics and psychiatric illness. HRV measures were assessed by time-domain and frequency-domain analyses. Psychological symptoms were measured using the Hamilton rating scale for anxiety (HAM-A), Hamilton rating scale for depression (HAM-D). In addition, the evaluation for lipid profile was performed by blood test. Results: In serum lipid profile test, MDD group showed higher cholesterol ($197.68{\pm}42.94$ mg/dL vs. $176.85{\pm}34.68$ mg/dL, p=0.044), TG ($139.45{\pm}92.54$ mg/dL vs. $91.4{\pm}65.68$ mg/dL, p=0.018), LDL ($130.03{\pm}33.18$ vs. $106.62{\pm}27.08$, p=0.004) level than normal control group. In HRV time domain analyses, the standard deviation of the NN interval (SDNN) was decreased in MDD group than normal control group, but was not significant ($32.82{\pm}14.33$ ms vs. $40.36{\pm}21.40$ms, p=0.078). ApEn (Approximate Entrophy) was significantly increased in MDD group than normal control group ($1.13{\pm}0.11$ vs. $0.91{\pm}0.18$, p<0.001). ApEn was correlated with LDL level (r=0.277, p=0.028), HAM-D scores (r=0.534, p<0.001) and HAM-A scores (r=0.470, p<0.001). Conclusions: MDD patients showed increased ApEn, one of the HRV measurement. And this ApEn was correlated with LDL, HAM-D and HAM-A scores. In this study, the analysis of ApEn would be a useful test of MDD.