• Proceedings of the Korean Society for Cognitive Science Conference
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• 2005.05a
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• pp.151-153
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• 2005

We investigated the brain substrate of repetition priming on the implicit memory taskusing low-resolution electromagnetic tomography (LORETA) with high-density 128 channel EEG and individual MRI as a realistic head model. Thirteen right-handed, healthy subjects performed a word/nonword discrimination task, in which the words and nonwords were presented visually,and some of the words appeared twice with a lag of one or five items. All of the subjects exhibited repetition priming with respect to the behavioral data, in which a faster reaction time was observed to the repeated word (old word) than to the first presentation of the word (new word). The old words elicited more positive-going potentials than the new words, beginning at 200 ms and lasting until 500 ms post-stimulus. We conducted source reconstruction using LORETA at a latency of 400 ms with the peak mean global field potentials and used statistical parametric mapping for the statistical analysis. We found that the source elicited by the old words exhibited a statistically significant current density reduction in the left inferior frontal gyrus. This is the first study to investigate the generators of repetition priming using voxel-by-voxel statistical mapping of the current density with individual MRI and high-density EEG.

• Water Engineering Research
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• v.2 no.2
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• pp.139-148
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• 2001

The teleconnections between El Nino/Southern Oscillation (ENSO) and droughts in Korea and the continental United States(U.S.) are investigated using cross analysis. For this purpose, monthly ENSO data and Palmer Drought Severity Index (PDSI) for Korea and for seven states in the U.S. are used. This study shows that there are significant statistical associations between ENSO indices and PDSI for Korea; however, the associations are very weak. It is found that dry conditions in Korea are positively correlated with El Nino, while wet conditions with La Nina. SOI, SSt in the Nino 4 and Ship track 6 regions among ENSO indices are more strongly correlated with PDSI than the other ENSO indices when using the original standardized data, but the SST Nino 3, SST Nino 4, and Darwin SSP exhibit abetter correlations with PDSI when using filtered data to be removed autocorrelation components of the original standardized data. The response time lag for maximum correlation between ENSO indices and PDSI appears to be affected by filtering the data. This is expecially true for Korea than for state analyzed in U.S. In addition, it is found that the PDSI in the continental U.S. is more strongly correlated wiht ENSO than in Korea. Furthermore, in analyzing the El Nino and La Nina aggregate composite data, it is found that the dry anomalies in Korea occur from the year following El Nino to about tow years after while the wet anomalies occur from La Nina year for a period of about two years.

• Journal of the Korean housing association
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• v.16 no.5
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• pp.29-38
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• 2005

Recently there has been an apparent paradigm shift in housing market towards customer oriented approach. In the midst of increasing competition, there is indeed a need to better understand of customers, and to quickly respond to their individual needs and wants. In this background, this paper aims to show a scientific marketing approach in housing industries, and to provide general information on Japanese condominium housing market in terms of a brief historical overview and recent market situation. Results include that there exists an about twenty year time lag in the start of condominium housing supply in the private sector between Korea and Japan. Besides, more efforts should be paid to the diversity or locality of the supplied housing type or design, especially condominium apartments in Korea compared to those of Japan. Among others this paper emphasized on illustrating the actual applications of analysis on consumers' purchasing behavior and latent conscious coupled with some statistical techniques, which may lead marketers or decision makers to forecast more accurate customers demands.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.1147-1154
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• 2021

The study aimed to investigate the impact of the microfinance program on loan borrowers' asset accumulation. In doing this, the study used descriptive and statistical methods to achieve the objectives. Primary data were collected from 192 respondents from Bangladesh using survey questionnaires. The data were analyzed using the multiple linear regression model. The result revealed that the majority of the borrowers said their assets such as farm, land, and livestock remained the same, which implies that microfinance borrowers still lag behind accumulating household assets. However, in the case of housing conditions and household appliances, there was a marginal increase. The regression result provides evidence that, among other factors, the amount of loan received from microfinance institutions and time duration with them is the most significant role-playing factor for borrower's sustainable well-being. The age and education level of borrowers are identified as positively related to asset accumulations, but not substantially so. Moreover, training provided by microfinance institutions is not effective and influential for microfinance borrowers' wealth accumulation, which is evidenced by the findings. This study's insights are worthwhile for any microfinance institution's decision-makers, development partners, and government to stress the shortcomings and accelerate the borrower's wealth status.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.5
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• pp.699-706
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• 2020

Predictive models in food microbiology are used for predicting microbial growth or death rates using mathematical and statistical tools considering the intrinsic and extrinsic factors of food. This study developed predictive growth models for Bacillus cereus on dried laver Pyropia pseudolinearis stored at different temperatures (5, 10, 15, 20, and 25℃). Primary models developed for specific growth rate (SGR), lag time (LT), and maximum population density (MPD) indicated a good fit (R²≥0.98) with the Gompertz equation. The SGR values were 0.03, 0.08, and 0.12, and the LT values were 12.64, 4.01, and 2.17 h, at the storage temperatures of 15, 20, and 25℃, respectively. Secondary models for the same parameters were determined via nonlinear regression as follows: SGR=0.0228-0.0069*T₁+0.0005*T₁²; LT=113.0685-9.6256*T₁+0.2079*T₁²; MPD=1.6630+0.4284*T₁-0.0080*T₁² (where T₁ is the storage temperature). The appropriateness of the secondary models was validated using statistical indices, such as mean squared error (MSE<0.01), bias factor (0.99≤B_f≤1.07), and accuracy factor (1.01≤A_f≤1.14). External validation was performed at three random temperatures, and the results were consistent with each other. Thus, these models may be useful for predicting the growth of B. cereus on dried laver.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.873-885
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• 2016

Onions are grown in a few specific regions of Korea that depend on the climate and the regional characteristic of the production area. Therefore, when onion yields are to be estimated, it is reasonable to use a statistical model in which both the climate and the region are considered simultaneously. In this paper, using a spatial panel regression model, we predicted onion yields with the different weather conditions of the regions. We used the spatial auto regressive (SAR) model that reflects the spatial lag, and panel data of several climate variables for 13 main onion production areas from 2006 to 2015. The spatial weight matrix was considered for the model by the threshold value method and the nearest neighbor method, respectively. Autocorrelation was detected to be significant for the best fitted model using the nearest neighbor method. The random effects model was chosen by the Hausman test, and the significant climate variables of the model were the cumulative duration time of sunshine (January), the average relative humidity (April), the average minimum temperature (June), and the cumulative precipitation (November).

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.13-20
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• 2012

In this study, the change pattern of hydrological response function as development has been observed. The target watershed was selected Tanbu sub-Basin in the Bocheong Basin. The applied channel networks are composed of 10 cases that are channel networks by strahler's ordering scheme and cases of all grids channel or the hillslope in basin. To each case of grid in basin, channel and hillslope drainage path lengths to outlet of basin are calculated, and hydrological response function was calculated by Nash Model. As results of this analysis, the peak discharge of hydrological response function is increased and peak time is shortened as development of channel networks. And based on statistical characteristics of hydrological response function, mean (lag time) and variance of travel time are reduced exponentially.

• YAKHAK HOEJI
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• v.42 no.5
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• pp.513-518
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• 1998

Fluoxetine is a nontricyclic antidepressant which blocks serotonin reuptake selectively. Its N-demethyl metabolite, norfluoxetine is also selective inhibitor of serotonin uptake . This study was carried out to compare the bioavailability of Myung-in fluoxetine (20mg/cap.) with that of Prozac$^{\circde{R}}$. The bioavailability was conducted on 24 healthy volunteers who received a single dose (80mg) of each drug in the fasting state, in a randomized balanced 2-way crossover design. After closing, serial blood samples were collected for a period of 48 hours, Plasma was analyzed for fluoxetine and norfluoxetine by a sensitive and validated HPLC assay. The major pharmacokinetic parameters ($AUC_{0-48\;hr}$, Cmax, Tmax , $AUC_{inf.}$, MRT. $T_{1/2}$, Vd and Cl) were, calculated from the plasma fluoxetine concentration-time data of each volunteer. The microcomputer program, 'WinNonlin' was used for compartmental analysis. A two-compartment model with first-order input, first-order output and no lag time was chosen as the most appropriate pharmacokinetic model. The data were best described by using a weighting factor of $1/y^2$. Though the plasma fluoxetine concentrations of Myung-in fluoxetine were higher than those of Prozac$^{\circde{R}}$ at all observed time from 7.9% to 16.9% (P<0.05 at 6.7 and 10 hr), the bioavailability of Myung-in fluoxetine appeared to be bioequivalent with that of Prozac$^{\circde{R}}$. There were no statistical significant differences between the two drugs in all pharmacokinetic parameters including $AUC_{0-48\;hr}$ of norfluoxetine.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.215-231
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• 2017

The US presidential primaries take place sequentially in different places with a time lag. However, they have not attracted as much attention in terms of modelling as the US presidential election has. This study applied several autologistic models to find the relation between the outcome of the primary election for a Democrat candidate with socioeconomic attributes in consideration of spatial and temporal dependence. According to the result applied to the 2016 election data at the county level, Hillary Clinton was supported by people in counties with high population rates of old age, Black, female and Hispanic. In addition, spatial dependence was observed, representing that people were likely to support the same candidate who was supported from neighboring counties. Positive auto-correlation was also observed in the time-series of the election outcome. Among several autologistic models of this study, the model specifying the effect of Super Tuesday had the best fit.

• Korean Journal of Soil Science and Fertilizer
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• v.23 no.2
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• pp.87-93
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• 1990

A statistical model to predict soil temperature from the ambient meteorological factors including mean, maximum and minimum air temperatures, precipitation, wind speed and snow depth combined with Fourier time series expansion was developed with the data measured at the Suwon Meteorolical Service from 1979 to 1988. The stepwise elimination technique was used for statistical analysis. For the yearly oscillation model for soil temperature with 8 terms of Fourier expansion, the mean square error was decreased with soil depth showing 2.30 for the surface temperature, and 1.34-0.42 for 5 to 500-cm soil temperatures. The $r^2$ ranged from 0.913 to 0.988. The number of lag days of air temperature by remainder analysis was 0 day for the soil surface temperature, -1 day for 5 to 30-cm soil temperature, and -2 days for 50-cm soil temperature. The number of lag days for precipitaion, snow depth and wind speed was -1 day for the 0 to 10-cm soil temperatures, and -2 to -3 days for the 30 to 50-cm soil teperatures. For the statistical soil temperature prediction model combined with the yearly oscillation terms and meteorological factors as remainder terms considering the lag days obtained above, the mean square error was 1.64 for the soil surfac temperature, and ranged 1.34-0.42 for 5 to 500cm soil temperatures. The model test with 1978 data independent to model development resulted in good agreement with $r^2$ ranged 0.976 to 0.996. The magnitudes of coeffcicients implied that the soil depth where daily meteorological variables night affect soil temperature was 30 to 50 cm. In the models, solar radiation was not included as a independent variable ; however, in a seperated analysis on relationship between the difference(${\Delta}Tmxs$) of the maximum soil temperature and the maximum air temperature and solar radiation(Rs ; $J\;m^{-2}$) under a corn canopy showed linear relationship as $${\Delta}Tmxs=0.902+1.924{\times}10^{-3}$$ Rs for leaf area index lower than 2 $${\Delta}Tmxs=0.274+8.881{\times}10^{-4}$$ Rs for leaf area index higher than 2.