• Child Health Nursing Research
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• v.19 no.1
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• pp.12-20
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• 2013

Purpose: The purpose of this study was to investigate factors associated with suspicious developmental delay in infants and early childhood. Methods: Participants were 133 infants, aged from birth to 6 years old and their mothers, who were being seen at 16 Public health centers in B city. Korean Denver II was used to test infant development. ${\chi}^2$-test, Fisher's exact test and multiple logistic regression were used with SPSS 19.0 to analyze data. Results: Of participant infants, 7.5% were below the 3rd percentile for the weight percentile, 8.4% is a weight curve that crosses more than 2 percentile lines on the growth charts after previous achievement, and 9.8% had suspicious developmental delay according to Korean Denver II. Further the predictive factors related to suspicious development delay in the children were decrease of weight percentile (Odds Ratio [OR]=6.69, Confidence Interval [CI])=1.22-36.45), low economic state (OR=6.26, CI=1.50-26.00), and development delay perceived by their mothers (OR=4.99, CI=1.24-20.06). Conclusion: It is necessary to build a government level system to follow management of development of infants and children from the time of birth. Especially, it is necessary to develop a program for children in low income families.

• Tuberculosis and Respiratory Diseases
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• v.84 no.2
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• pp.125-133
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• 2021

Background: The coronavirus disease (COVID-19) can manifest in a range of symptoms, including both asymptomatic systems which appear nearly non-existent to the patient, all the way to the development of acute respiratory distress syndrome (ARDS). Specifically, COVID-19-associated pneumonia develops into ARDS due to the rapid progression of hypoxia, and although arterial blood gas analysis can assist in halting this deterioration, the current environment provided by the COVID-19 pandemic, which has led to an overall lack of medical resources or equipment, has made it difficult to administer such tests in a widespread manner. As a result, this study was conducted in order to determine whether the levels of oxygen saturation (SpO₂) and the fraction of inhaled oxygen (FiO₂) (SF ratio) can also serve as predictors of ARDS and the patient's risk of mortality. Methods: This was a retrospective cohort study conducted from February 2020 to Mary 2020, with the study's subjects consisting of COVID-19 pneumonia patients who had reached a state of deterioration that required the use of oxygen therapy. Of the 100 COVID-19 pneumonia cases, we compared 59 pneumonia patients who required oxygen therapy, divided into ARDS and non-ARDS pneumonia patients who required oxygen, and then investigated the different factors which affected their mortality. Results: At the time of admission, the ratios of SpO₂, FiO₂, and SF for the ARDS group differed significantly from those of the non-ARDS pneumonia support group who required oxygen (p<0.001). With respect to the predicting of the occurrence of ARDS, the SF ratio on admission and the SF ratio at exacerbation had an area under the curve which measured to be around 85.7% and 88.8% (p<0.001). Multivariate Cox regression analysis identified that the SF ratio at exacerbation (hazard ratio [HR], 0.916; 95% confidence interval [CI], 0.846-0.991; p=0.029) and National Early Warning Score (NEWS) (HR, 1.277; 95% CI, 1.010-1.615; p=0.041) were significant predictors of mortality. Conclusion: The SF ratio on admission and the SF ratio at exacerbation were strong predictors of the occurrence of ARDS, and the SF ratio at exacerbation and NEWS held a significant effect on mortality.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.533-540
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• 2010

Dam is very important facility in water supply and flood control. Therefore study needs to analyze reservoir capacity accurately to manage Dam efficiently. This study compared time series reservoir capacity using multi-dimensional spatial information to Chungju Dam reservoir and major conclusions are as follows. First, LiDAR and multi beam echo sounder survey were carried out in land zone and water zone of Dam reservoir area. And calibration process was performed to enhance the accuracy of survey data and it could be constructed that multi dimensional spatial information which was clearly satisfied with the standard of tolerance error by validation with ground control points. Reservoir capacity by water level was calculated using triangle irregular network from detailed topographic data that was constructed by linked with airborne LiDAR and multi beam echo sounder data, and curve equation of reservoir capacity was developed through regression analysis in 2008. In the comparison of the reservoir capacity of 2008 with those of 1986 and 1996, the higher water level goes, total reservoir capacity of 2008 showed decrease because of the increase of sediment in reservoir. Also, erosion and sediment area could be analyzed through calculating the reservoir capacity by the range of water level. Especially the range of water level as 130.0~135.0 which is the upper part of average water level, showed the highest erosion characteristics during 1986~2008 and 1996~2008 and it is considered that the erosion of reservoir slant by heavy rainfall is major reason.

• Korean Journal of Radiology
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• v.23 no.4
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• pp.466-478
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• 2022

Objective: ¹⁸F-fluorodeoxyglucose (FDG) PET/CT is often used for detecting malignancy in patients with newly diagnosed hemophagocytic lymphohistiocytosis (HLH), with acceptable sensitivity but relatively low specificity. The aim of this study was to improve the diagnostic ability of ¹⁸F-FDG PET/CT in identifying malignancy in patients with HLH by combining ¹⁸F-FDG PET/CT and clinical parameters. Materials and Methods: Ninety-seven patients (age ≥ 14 years) with secondary HLH were retrospectively reviewed and divided into the derivation (n = 71) and validation (n = 26) cohorts according to admission time. In the derivation cohort, 22 patients had malignancy-associated HLH (M-HLH) and 49 patients had non-malignancy-associated HLH (NM-HLH). Data on pretreatment ¹⁸F-FDG PET/CT and laboratory results were collected. The variables were analyzed using the Mann-Whitney U test or Pearson's chi-square test, and a nomogram for predicting M-HLH was constructed using multivariable binary logistic regression. The predictors were also ranked using decision-tree analysis. The nomogram and decision tree were validated in the validation cohort (10 patients with M-HLH and 16 patients with NM-HLH). Results: The ratio of the maximal standardized uptake value (SUVmax) of the lymph nodes to that of the mediastinum, the ratio of the SUVmax of bone lesions or bone marrow to that of the mediastinum, and age were selected for constructing the model. The nomogram showed good performance in predicting M-HLH in the validation cohort, with an area under the receiver operating characteristic curve of 0.875 (95% confidence interval, 0.686-0.971). At an appropriate cutoff value, the sensitivity and specificity for identifying M-HLH were 90% (9/10) and 68.8% (11/16), respectively. The decision tree integrating the same variables showed 70% (7/10) sensitivity and 93.8% (15/16) specificity for identifying M-HLH. In comparison, visual analysis of ¹⁸F-FDG PET/CT images demonstrated 100% (10/10) sensitivity and 12.5% (2/16) specificity. Conclusion: ¹⁸F-FDG PET/CT may be a practical technique for identifying M-HLH. The model constructed using ¹⁸F-FDG PET/CT features and age was able to detect malignancy with better accuracy than visual analysis of ¹⁸F-FDG PET/CT images.

• Korean Journal of Culture and Social Issue
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• v.28 no.2
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• pp.187-217
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• 2022

The aim of this study is to examine the psychometric properties of the Korean version of the Post-traumatic Stress Disorder Checklist for DSM-5(PCL-5). For this purpose, online surveys were conducted for two times with a one year interval using the data from 1,077 Korean adults at time 1, and 563 Korean adults at time 2. First, from the result of the confirmatory factor analysis, comparing the model fit of the 1, 4, 6, and 7-factor model, the 4, 6, and 7-factor model showed a acceptable fit, and the best fit was seen in the order of the 7, 6, 4-factor model. Second, the internal consistency, omega coefficient, construct validity, average variance extracted, and test-retest reliability results were all satisfactory.. Third, a correlation analysis with the K-PC-PTSD-5 and the sub-factors of BSI-18 was conducted to check the validity of the Korean Version of PCL-5. As a result, a positive correlation was seen with both K-PC-PTSD-5 and BSI-18. Fourth, a hierarchical multiple regression was performed to examine whether the Korean Version of PCL-5 predicts future PTSD, depression, anxiety, and somatization. As a result, the Korean Version of PCL-5 measured at time 1 significantly predicted PTSD, depression, anxiety, and somatization symptoms at time 2. Fifth, by analyzing the ROC curve, the discriminant power of PCL-5 for screening PTSD symptom groups was confirmed, and the best cut-off score was suggested. As a result of the longitudinal validation of Korean version of PCL-5, it was found that this scale is a reliable and valid measure for Korean adults. By looking into the predictive validity of the scale, it was found that the Korean version of PCL-5 can predict not only PTSD symptoms but also PTSD-related symptoms such as depression, anxiety, and somatization. Also, this study differs from previous validation studies measuring PTSD symptoms in that it suggested a cut-off score to help differentiate PTSD symptom groups.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.2 no.2
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• pp.147-154
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• 1969

The larvae of the ark shell Anadare broughtoni(Schrenck) were grown at room temporature (approximately $20.4^{\circ}C$), and fed laboratory-cultured Cyclotella nana. The egg of the ark shell produced in the laboratory measured about $54.9\mu$ in diameter. The embryos gradually developed into larvae up to $110.8\mu$ shell length, $83.9\mu$ shell height and with shell breadth of $58.2\mu$ even in the absence of the algal food. Beyond this sire, however, the growth of the larvae was considerably retarded. The larvae showed better growth rate when they were fed the algal food two days after spawning, i. e., early straight-hinge stage. Daily rate of food consumption varies according to the larval sizes. But the rate increases considerably when the larvae begin to form umbos. In general the rate Is indicated by the following formula: $Y=0.0025161\;X^{2.76459}$. The growth experiments of the larvae indicate that the efficiency of food conversion was higher when fed centrifuged food. Regarding to the difference in the slopes of growth curve, centrifuged food showed better growth rate as compared to those grown with the non-centrifuged food. The smaller the larval size, the greater will be the difference in growth. The larvae began settling when they reathed 261.7 to $289.6\;{\mu}$ in shell length, 199.2 to $221.7\mu$ in shell height and 147.6 to $170.8\mu$ in shell breadth. The time which elapsed from spawning to the larval settlement was about 28 days. The mean growth of the larvae is indicated with regression line and exponential curve equations as follows. Regression line shell length. 94.3 to $133.9\mu$ : Y==85.22857+3.35000X 141.6 to $269.3\mu$: Y=10.83036X-36.05357 296.8 to $373.2\mu$ : Y=19.10000X-279.30000 shell height: 72.7 to $89.7\mu$ : Y=67.11429+2.15714X 108.4 to $206.4\mu$ : Y=8.31607X-27.45357 228.6 to $282.1\mu$: Y=173.46700+13.37500X shell breadth: 45.3 to $77.8\mu$ : Y=38.08510X+2.73570X 87.4 to $157.7\mu$: Y=5.77320X-5.99640 175.4 to $214.0\mu$: Y=19.65000X-114.13300 Exponential curve shell length. 94.3 to $373.2\mu$: Y=72.45 $e^{0.04697x}$ shell height: 72.7 to $282.1\mu$: Y=54,96 $e^{0.04720x}$ shell breadth: 45.3 to $214.0\mu$ : Y=39.82 $e^{0.04927x}$ The relationships between the shell length and shell height and between the shell length and shell breadth are indicated as follows- shell height: 72.7 to $98.7\mu$ : Y=12.87780+0.63817X 108.4 to $206.4\mu$ : Y=0.90220+0.76456X 228.6 to $282.1\mu$ : Y=25.02630+0.69156X shell breadth: 45.3 to $77.8\mu$:Y=0.81373Xx-31.18914 87.4 to $157.7\mu$ : Y=13.37549+0.53230X 175.4 to $214.0\mu$: Y=30.24328+0.49545X

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

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.4
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• pp.225-235
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• 2023

Critical temperature for grain filling of three Korean japonica rice varieties was analyzed by late transplantation to induce a low temperature during grain filling period in a field study. The grain filling percentage (GFP) and grain dry weight (GDW) showed a sigmoid development for the first and second transplants in 2020 and first to third transplants in 2021. For the third transplant in 2020 and fourth transplant in 2021, the GFP and GDW reached a peak at 42 days after heading (DAH), and then decreased or remained unchanged. In non-linear regression analyses, 95% of the final GFP and GDW appeared at 49 - 62 DAH for the second transplant in 2020 and at 37 - 46 DAH and 30 - 36 DAH for the second and third transplants in 2021, respectively. At these time points, the critical seven-day moving temperature (7dMovT) was 8.4~9.4℃ for the second transplant in 2020 and 9.4~10.9℃ for the second and third transplants in 2021, depending on the grain filling traits and varieties of rice. The lowest 7dMovT was 8.4 - 8.7℃ in the three varieties. The observed lowest 7-day mean temperature (7dMT) accompanying increases in the GFP and GDW was 9.4 - 10.1℃, depending on the varieties in the third transplant of 2020 and fourth transplant of 2021. In the two transplants mentioned above, the highest 7dMT that showed no increase or decrease in grain filling traits was 8.7 - 9.1℃. The critical temperature for grain filling of japonica rice was 8.4 - 8.7℃ based on the 7dMovT and 9.1 - 9.4℃ based on the 7dMT. The previous 7dMT of 10℃ is recommended to determine the marginal harvest time for safe rice cropping since the temperature was the highest among the lowest temperature that accompanied an increase in grain filling traits.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Analytical Science and Technology
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• v.31 no.4
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• pp.161-170
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• 2018

The epidemic of disorders associated with synthetic stimulants, such as methamphetamine (MA) and amphetamine (AP), is a health, social, legal, and financial problem. Owing to the high potential of their abuse and addiction, reliable analytical methods are required to detect and identify MA, AP, and their metabolites in biological samples. Thus, a dilute-and-shoot liquid chromatography-tandem mass spectrophotometry (LC-MS/MS) was developed for simultaneous determination of MA, 4-hydroxymethamphetamine (4HMA), AP, and 4-hydroxyamphetamine (4HA) in urine. Urine sample ($100{\mu}L$) was mixed with $50{\mu}L$ of mobile phase consisting of 0.4 % formic acid and methanol and $50{\mu}L$ of working internal-standard solution. Aliquots of $8{\mu}L$ diluted urine was injected into the LC-MS/MS system. For all analytes, chromatographic separation was performed using a C18 reversed-phase column with gradient elution and a total run time of 5 min. The identification and quantification were performed by multiple reaction monitoring (MRM). Linear least-squares regression was conducted to generate a calibration curve, with $1/x^2$ as the weighting factor. The linear ranges were 2.0-200, 1.0-800, and 10-2500 ng/mL for 4HA and 4HMA, AP, and MA, respectively. The inter- and intraday precisions were within 6.6 %, whereas the inter- and intraday accuracies ranged from -14.9 to 11.3 %. The low limits of quantification were 2.0 ng/mL (4HA and 4HMA), 1.0 ng/mL (AP), and 10 ng/mL (MA). The proposed method exhibited satisfactory selectivity, dilution integrity, matrix effect, and stability, which are required for validation. Moreover, the purification efficiency of high-speed centrifugation was clearly higher than 6-15 % for QC samples (n=5), which was higher than that of the membrane-filtration method. The applicability of the proposed method was tested by forensic analysis of urine samples from drug abusers.