• Investigative Magnetic Resonance Imaging
• /
• v.18 no.1
• /
• pp.1-6
• /
• 2014

Purpose : $T_2{^*}$ relaxation time which includes susceptibility information represents unique feature of tissue. The objective of this study was to investigate $T_2{^*}$ relaxation times of the normal glandular tissue and fat of breast using a 3T MRI system. Materials and Methods: Seven-echo MR Images were acquired from 52 female subjects (age $49{\pm}12 $years; range, 25 to 75) using a three-dimensional (3D) gradient-echo sequence. Echo times were between 2.28 ms to 25.72 ms in 3.91 ms steps. Voxel-based $T_2{^*}$ relaxation times and $R_2{^*}$ relaxation rate maps were calculated by using the linear curve fitting for each subject. The 3D regions-of-interest (ROI) of the normal glandular tissue and fat were drawn on the longest echo-time image to obtain $T_2{^*}$ and $R_2{^*}$ values. Mean values of those parameters were calculated over all subjects. Results: The 3D ROI sizes were $4818{\pm}4679$ voxels and $1455{\pm}785$ voxels for the normal glandular tissue and fat, respectively. The mean $T_2{^*}$ values were $22.40{\pm}5.61ms$ and $36.36{\pm}8.77ms$ for normal glandular tissue and fat, respectively. The mean $R_2{^*}$ values were $0.0524{\pm}0.0134/ms$ and $0.0297{\pm}0.0069/ms$ for the normal glandular tissue and fat, respectively. Conclusion: $T_2{^*}$ and $R_2{^*}$ values were measured from human breast tissues. $T_2{^*}$ of the normal glandular tissue was shorter than that of fat. Measurement of $T_2{^*}$ relaxation time could be important to understand susceptibility effects in the breast cancer and the normal tissue.

• Nuclear Medicine and Molecular Imaging
• /
• v.41 no.4
• /
• pp.317-325
• /
• 2007

Purpose: Biological parameters can be quantified using dynamic PET data with compartment modeling and Nonlinear Least Square (NLS) estimation. However, the generation of parametric images using the NLS is not appropriate because of the initial value problem and excessive computation time. In irreversible model, Patlak graphical analysis (PGA) has been commonly used as an alternative to the NLS method. In PGA, however, the start time ($t^*$, time where linear phase starts) has to be determined. In this study, we suggest a new Multiple Linear Analysis for irreversible radiotracer (MLAIR) to estimate fluoride bone influx rate (Ki). Methods: $[^{18}F]Fluoride$ dynamic PET scans was acquired for 60 min in three normal mini-pigs. The plasma input curve was derived using blood sampling from the femoral artery. Tissue time-activity curves were measured by drawing region of interests (ROls) on the femur head, vertebra, and muscle. Parametric images of Ki were generated using MLAIR and PGA methods. Result: In ROI analysis, estimated Ki values using MLAIR and PGA method was slightly higher than those of NLS, but the results of MLAIR and PGA were equivalent. Patlak slopes (Ki) were changed with different $t^*$ in low uptake region. Compared with PGA, the quality of parametric image was considerably improved using new method. Conclusion: The results showed that the MLAIR was efficient and robust method for the generation of Ki parametric image from $[^{18}F]Fluoride$ PET. It will be also a good alternative to PGA for the radiotracers with irreversible three compartment model.

• Journal of Animal Science and Technology
• /
• v.50 no.4
• /
• pp.561-572
• /
• 2008

Responses of real-time control parameters, such as ORP, DO and pH, to the conditions of biological animal wastewater treatment process were examined to evaluate the stability of real-time control using each parameter. Also an optimum index for supplemental carbon source addition based on NOx-N level was determined under a consideration of denitrification rate by endogenous respiration of microorganism and residual organic matter in liquor. Experiment was performed with lab-scale sequencing batch reactor(SBR) and working volume of the process was 45L. The distinctive nitrogen break point(NBP) on ORP-and DO-time profiles, which mean the termination of nitrification, started disappearing with the maintenance of low NH4-N loading rate. Also the NBP on ORP-and DO-time profiles was no longer observed when high NOx-N was loaded into the reactor, and the sensitivity of ORP became dull with the increase of NOx-N level. However, the distinctive NBP was constantly occurred on pH(mV)-time profile, maintaining unique profile patterns. This stable occurrence of NBP on pH(mV)-time profile was lasted even at very high NOx-N:NH4-N ratio(over 80:1) in reactor, and the specific point could be easily detected by tracking moving slope change(MSC) of the curve. Revelation of NBP on pH(mV)-time profile and recognition of the realtime control point using MSC were stable at a condition of over 300mg/L NOx-N level in reactor. The occurrence of distinctive NBP was persistent on pH(mV)-time profile even at a level of 10,000mg/L STOC(soluble total organic carbon) and the recognition of NBP was feasible by tracing MSC, but that point on ORP and DO-time profiles began to disappear with the increase of STOC level in reactor. The denitrfication rate by endogenous respiration and residual organic matter was about 0.4mg/L.hr., and it was found that 0.83 would be accepted as an index for supplemental carbon source addition when 0.1 of safety factor was applied.

• Horticultural Science & Technology
• /
• v.28 no.5
• /
• pp.768-776
• /
• 2010

This study was conducted to predict number and fresh weight of leaves, and total leaf area of a single-stemmed rose 'Vital' based on the accumulated thermal units, and to develop a model of shoot development for the prediction of the time when the flowering shoot reaches a phenological stage in a plant factory system. The base temperature ($T_b$), optimum temperature ($T_{opt}$), and maximum temperature ($T_{max}$) were estimated by regressing the rate of shoot development against the temperature gradient. The rate of shoot development ($R$, $d^{-1}$) for the phase from cutting to bud break (CT-BB) was best described by a linear model $R_b$ ($d^{-1}$) = -0.0089 + $0.0016{\cdot}temp$. The rate of shoot development for the phase from bud break to harvest (BB-HV) was fitted to the parabolic model $R_h$ ($d^{-1}$) = $-0.0001{\cdot}temp^2$ + $0.0054{\cdot}temp$ - 0.0484. The $T_b$, $T_{opt}$, and $T_{max}$ values were 5.56, 27.0, and $42.7^{\circ}C$, respectively. The $T_b$ value was used in the thermal unit computations for the shoot development. Number of leaves, leaf area (LA), and leaf fresh weight showed sigmoidal curves regardless of the cut time. The shoot development and leaf area model was described as a sigmoidal function using thermal units. Leaf area was described as LA = 578.7 $[1+(thermal units/956.1)^{-8.54}]^{-1}$. Estimated and observed shoot length and leaf fresh weight showed a reasonably good fit with 1.060 ($R^2=0.976^{***}$) and 1.043 ($R^2=0.955^{***}$), respectively. The average thermal units required from cutting to transplant and from transplant to harvest stages were $426{\pm}42^{\circ}C{\cdot}d$ and $783{\pm}24^{\circ}C{\cdot}d$, respectively.

• Tuberculosis and Respiratory Diseases
• /
• v.50 no.4
• /
• pp.415-425
• /
• 2001

Background : The correlation between the high resolution computed tomography(HRCT) emphysema score and the physiologic parameters including resting and exercise pulmonary function test was investigated in 14 patients($60.6{\pm}10.3$ years) with pulmonary emphysema. Methods : The patients underwent a HRCT, a resting pulmonary function test, and incremental exercise testing(cycle ergometer, 10 W/min). Computed tomography scans were obtained on a GE highlight at 10 mm intervals using 10 mm collimation, from the apex to the base after a full inspiration. The emphysema scores were determined by a CT program 'Density mask' outlining the areas with attenuation values less than -900 HU, indicating the emphysema areas, and providing an overall percentage of lung involvement by emphysema. Results : Among the resting PFT parameters, only the diffusing capacity(r=-0.75) and $PaO_2$ (r=-0.66) correlated with the emphysema score(p<0.05). Among the exercise test parameters, the emphysema score correlated significantly with the maximum power(r=-0.74), maximum oxygen consumption(r=-0.68), anaerobic threshold(V-slope method: r=-0.69), maximal $O_2$-pulse(r=-0.73), and the physiologic dead space ratio at the maximum workload(r=-0.80)(p<0.01). Conclusion: We could find that exercise testing parameters showed a much better correlation with the HRCT emphysema score, which is known to have a good correlation with the pathologic severity than the resting PIT parameters. Therefore it is suggested that exercise testing is superior to resting PIT for estimating in the estimation of the physiologic disturbance in emphysema patients.

• Journal of Radiation Protection and Research
• /
• v.40 no.4
• /
• pp.194-201
• /
• 2015

We have investigated the EPR signal properties in 12 components of two mobile phones (LCD, OLED) using electron paramagnetic resonance (EPR) spectrometer in this study.EPR measurements were performed at normal atmospheric conditions using Bruker EXEXSYS-II E500 spectrometer with X-band bridge, and samples were irradiated by $^{137}Cs$ gamma-ray source. To identify the presence of radiation-induced signal (RIS), the EPR spectra of each sample were measured unirradiated and irradiated at 50 Gy. Then, dose-response curve and signal intensity variating by time after irradiation were measured. As a result, the signal intensity increased after irradiation in all samples except the USIM plastic and IC chip. Among the samples, cover glass(CG), lens, light guide plate(LGP) and diffusion sheet have shown fine linearity ($R^2$ > 0.99). Especially, the LGP had ideal characteristics for dosimetry because there were no signal in 0 Gy and high rate of increase in RIS. However, this sample showed weakness in fading. Signal intensity of LGP and Diffusion Sheet decreased by 50% within 72 hours after irradiation, while signals of Cover Glass and Lens were stably preserved during the short period of time. In order to apply rapidly EPR dosimetry using mobile phone components in large-scale radiation accidents, further studies on signal differences for same components of the different mobile phone, fading, pretreatment of samples and processing of background signal are needed. However, it will be possible to do dosimetry by dose-additive method or comparative method using unirradiated same product in small-scale accident.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.14 no.1
• /
• pp.50-56
• /
• 1994

This study was investigated for the purposes of securing forage resource using idled rice paddy recently increased in accordance to a current trend of farm products' liberalization and also of presevation of environment by using cattle sluny as liquid manure, which is seriously increasing these days. In this study, mean annual dry matter yield and its seasonal variation with reed canarygrass, and a optimum rate of cattle sluny application were investigated. The results are as follows: 1. According to the conditions of cutting frequencies(3, 4 and 5 cutting per year), mean annual dry matter yield was recorded from 8.9 tons to 10.9 tons per hectare and was the highest at 3 cutting frequency. 2. The use of cattle sluny with the levels of between 300 and 360 kg N per hectare showed a significantly higher mean annual dry matter yield than that of the control (non-fertilization). 3. The treatments with 3 and 4 cutting frequencies(90 kg Nhdyear, 120 kg Nhdyear) recorded higher dry matter yields than the control of the former level by 1.23 tons and 2.34 tons respectively and in the treatment of 5 cutting frequency, the second level with cattle sluny of 300 kg Nhdyear showed an increased dry matter yield of 2.11 tons compared to the former level(l50 kg Nhdyear). With regards to nitrogen efficiency, one kg of nitrogen is applied to 13.7, 19.4 and 14.1 kg of dry matter yields in the conditions of 3, 4 and 5 cutting frequencies respectively. 4. In view of seasonal variance of annual dry matter yield, the second cut in 3 cutting frequency, the third cut in 4 cutting frequency and the third in 5 cutting frequency showed the highest ratio as 42, 37 and 32% respectively compared to the total. 5. Under the conditions of this study, the 'Input-Output curve' from 5 cutting frequency was the closest to sigmaformed process(i=0.9993) of various cutting frequencies, and the maximum marginal yield in the treatment was obtained at the level of 250 kg Nha with cattle sluny. The economic level of cattle sluny was between 371.0 and 402.2 kg N and the highest dry matter yield was obtained at 489.3 kg Mdyear in the same treatment

• Korean Journal of Food Science and Technology
• /
• v.44 no.6
• /
• pp.658-665
• /
• 2012

A survey of zearalenone contamination was conducted on cereal-based products by using an immunoaffinity column with LC-MS/MS. The calibration curve showed good lineality, with correlation coefficients ($R^2$) of 0.999 in the concentration range from 1 to 250 ng/mL. The limits of detection and quantification were approximately $0.3{\mu}g/kg$ and $1.0{\mu}g/kg$, respectively. The recoveries in the barley tea, Misutgaru and snack ranged from 73.6-107.8%. Zearalenone was detected in 10 samples (11.2% incidence). The highest zearalenone contamination level was $29.7{\mu}g/kg$ in the Misutgaru. This survey was conducted with uncertainty of measurement. The expanded uncertainty for zearalenone was estimated to be $44.9{\pm}5.0{\mu}g/kg$ (k=2, 95% confidence level) and $128.7{\pm}7.9{\mu}g/kg$ (k=2, 95% confidence level) for barley tea, $30.7{\pm}5.8{\mu}g/kg$ (k=2, 95% confidence level) and $173.7{\pm}14.9{\mu}g/kg$ (k=2.26, 95% confidence level) for Misutgaru, and $37.2{\pm}7.4{\mu}g/kg$ (k=2.31, 95% confidence level) and $151.0{\pm}10.4{\mu}g/kg$ (k=2, 95% confidence level) snack at the level of $41.7{\mu}g/kg$ and $166.7{\mu}g/kg$, respectively.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.139-153
• /
• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Korean Journal of Environmental Biology
• /
• v.41 no.4
• /
• pp.505-518
• /
• 2023

The key to invasive pest management lies in preemptive action. However, most current research using species distribution models is conducted after an invasion has occurred. This study modeled the potential distribution of the globally notorious sweet potato pest, the sweet potato weevil(Cylas formicarius), that has not yet invaded Korea using MaxEnt. Using global occurrence data, bioclimatic variables, and topsoil characteristics, MaxEnt showed high explanatory power as both the training and test areas under the curve exceeded 0.9. Among the environmental variables used in this study, minimum temperature in the coldest month (BIO06), precipitation in the driest month (BIO14), mean diurnal range (BIO02), and bulk density (BDOD) were identified as key variables. The predicted global distribution showed high values in most countries where the species is currently present, with a significant potential invasion risk in most South American countries where C. formicarius is not yet present. In Korea, Jeju Island and the southwestern coasts of Jeollanam-do showed very high probabilities. The impact of climate change under shared socioeconomic pathway (SSP) scenarios indicated an expansion along coasts as climate change progresses. By applying the 10th percentile minimum training presence rule, the potential area of occurrence was estimated at 1,439 km² under current climate conditions and could expand up to 9,485 km² under the SSP585 scenario. However, the model predicted that an inland invasion would not be serious. The results of this study suggest a need to focus on the risk of invasion in islands and coastal areas.