• Clinical and Experimental Reproductive Medicine
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• v.24 no.2
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• pp.153-165
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• 1997

In male reproducible health, fertility and IVF (in-vitro fertilization), semen analysis has been most important. Semen analysis can be divided into concentration, motional and morphological analysis of sperm. The existing method which was developed earlier to analyze semen concentrated on the sperm motility analysis. To provide more useful and precise solutions for clinical problems such as infertility, semen analysis must include sperm morphological analysis. But the traditional tools for semen analysis are subjective, imprecise, inaccurate, difficult to standardize, and difficult to reproduce. Therefore, with the help of development of microcomputers and image processing techniques, we developed a new sperm morphology analyzer to overcome these problems. In this study the agreement on percent normal morphology was studied between different observers and a computerized sperm morphology analyzer on a slide-by-slide basis using strict criteria. Slides from 30 different patients from the SNUH andrology laboratory were selected randomly. Microscopic fields and sperm cells were chosen randomly and percent normal morphology was recorded. The ability of sperm morphology analyzer to repeat the same reading for normal and abnormal cells was studied. The results showed that there was no significant bias between two experienced observers. The limits of agreement were 4.1%${\sim}$-3.8%. The Pearson correlation coefficient between readers was 0.79. Between the manual and sperm morphology analyzer, the same findings were reported. In this experiments the slides were stained by two different methods, PAP and Diff-Quik staining methods. The limits of agreement were 7.2%${\sim}$-5.7% and 6.0%${\sim}$-6.3%, respectively. The Pearson correlation coefficients ware 0.76 and 0.91, respectively. The limits of agreement was tighter below 20% normal forms. In the experiments of repeatability, 52 cells stained by PAP and Diff-Quik staining methods were analyzed three times in succession. Estimating pairwise agreement, the kappa statistic for the pairs were 0.76, 0.81, 0.86, and 0.75, 0.88, 0.88 respectively. In this study it was shown that there was good agreement between manual and computerized assessment of normal and abnormal cells. The repeatability and agreement per slide of computerized sperm morphology analyzer was excellent. The computer's ability to classify normal morphology per slide is promising. Based on results obtained, this system can be of clinical value both in andrology laboratories and IVF units.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.997-1007
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• 2018

Drought monitoring is the important system for disasters by climate change. To perform this, it is necessary to measure the precipitation based on satellite rainfall estimation. The data developed in this study provides two kinds of satellite data (raw satellite data and bias-corrected satellite data). The spatial resolution of satellite data is 10 km and the temporal resolution is 1 day. South Korea was selected as the target area, and the original satellite data was constructed, and the bias-correction method was validated. The raw satellite data was constructed using TRMM TMPA and GPM IMERG products. The GRA-IDW was selected for bias-correction method. The correlation coefficient of 0.775 between 1998 and 2017 is relatively high, and TRMM TMPA and GPM IMERG 10 km daily rainfall correlation coefficients are 0.776 and 0.753, respectively. The BIAS values were found to overestimate the raw satellite data over observed data. By using the technique developed in this study, it is possible to provide reliable drought monitoring to Korean peninsula watershed. It is also a basic data for overseas projects including the un-gaged regions. It is expected that reliable gridded data for end users of drought management.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.3
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• pp.383-388
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• 2021

In this study, constant head water permeability test was adopted to evaluate self-healing performance of mortars containing inorganic healing materials which consist of blast furnace slag, sodium sulfate and anhydrite. Clinker powder and sand replaced for a part of cement and fine aggregates. On constant head water permeability test for self-healing mortars, unit water flow rate of mortar specimens were measured according to crack width and healing period. As a result of evaluating the healing performance of self-healing mortar, it was confirmed that with the initial crack width of 0.3mm, the healing rate at healing period of 28 days increased by more than 30%p compared to plain mortar, greatly improving the healing performance. Furthermore, the coefficient(α) which was estimated from the relationship between crack width and unit water flow rate was used for calculating equivalent crack width. By analyzing the correlation of healing rate and equivalent crack width, the time and initial crack width attaining healing target crack width were predicted.

• Korean Journal of Agricultural Science
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• v.48 no.2
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• pp.299-310
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• 2021

Glycerol is a non-volatile compound with no aromatic properties that contributes significantly to the quality of wine by providing sweetness and richness of taste. In addition, it is also the third most significant byproduct of alcoholic fermentation in terms of quantity after ethanol and carbon dioxide. In this study, Fourier transform infrared (FT-IR) spectroscopy was employed as a fast non-destructive method in conjugation with multivariate regression analysis to build a model for the quantitative analysis of glycerol concentration in wine samples. The samples were prepared by using three varieties of red wine samples (i.e., Shiraz, Merlot, and Barbaresco) that were adulterated with glycerol in concentration ranges from 0.1 to 15% (v·v^-1), and subjected to analysis together with pure wine samples. A net analyte signal (NAS)-based methodology, called hybrid linear analysis in the literature (HLA/GO), was applied for predicting glycerol concentrations in the collected FT-IR spectral data. Calibration and validation sets were designed to evaluate the performance of the multivariate method. The obtained results exhibited a high coefficient of determination (R²) of 0.987 and a low root mean square error (RMSE) of 0.563% for the calibration set, and a R² of 0.984 and a RMSE of 0.626% for the validation set. Further, the model was validated in terms of sensitivity, selectivity, and limits of detection and quantification, and the results confirmed that this model can be used in most applications, as well as for quality assurance.

• Applied Chemistry for Engineering
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• v.33 no.5
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• pp.477-481
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• 2022

As a part of improving the quality of crude methylnaphthalene (CMNO), this study was experimentally examined the reduction of nitrogen-containing compounds (NC) present in the CMNO by solvent extraction. The CMNO was composed of three kinds of NC [quinolone (QU), iso-quinoline (IQU), indole (IN)], three kinds of bicyclic aromatic compound [BAC; naphthalene (NA), 1-methylnaphthalene (1MNA), 2-methylnaphthalene (2MNA)] and biphenyl (BP) etc., in addition to an aqueous formamide solution, which were used as raw materials and a solvent, respectively. The increase in the volume fraction of water to the solvent in the initial state (y_w,0) caused a sharp decrease in the distribution coefficient and the yield of NC, but conversely raised the increased selectivity of NC based on 2MNA. The compositions of QU, IQU and IN in the raffinate oil recovered through the equilibrium extraction of batch co-current 5-stage under constant conditions [y_w,0 = 0.1, volume fraction of solvent to feed (CMNO) at the initial state = 1, operating temperature = 303 K, liquid-liquid contacting time = 72 h] were reduced by about 51.5%, 55.2%, and 71.8%, respectively, when compared to those of CMNO. From the excellent reduction rate of NC, the formamide extraction method suggested in this study can be expected to be a useful reduction method for NC contained in the CMNO.

• Journal of Nutrition and Health
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• v.56 no.4
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• pp.391-403
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• 2023

Purpose: This study evaluated the validity of a physical activity classification table (PACT) based on total energy expenditure (TEE) and physical activity level (PAL) measured using the doubly labeled water (DLW) method in Korean adults and the elderly. Methods: A total of 141 (male 70, female 71) adults and elderly were included. The reference standards TEE_DLW, PAL_DLW were measured over a 14-day period using DLW. A 24-hour physical activity diary was kept for three days (two days during the week and one day on the weekend). PAL_PACT was calculated by classifying the activity type and intensity using the PACT. PAL_PACT was multiplied by resting energy expenditure measured by indirect calorimetry to estimate TEE_PACT. Results: The mean age of the study participants was 50.5 ± 18.8 years, and the mean body mass index was 23.4 ± 3.3 kg/m². A comparison of TEE_DLW and TEE_PACT by sex and age showed no significant differences. The bias, the difference between TEE_DLW and TEE_PACT, was male 17.3 kcal/day and female -4.5 kcal/day. The percentage of accurate predictions (values within ± 10% of the TEE_DLW) of TEE_PACT was 58.6% in males and 54.9% in females, with the highest prediction values in the age group 40-64 years (70.9%) in males and over 65 years (73.9%) in females. The spearman correlation coefficient (r) between TEE_PACT and TEE_DLW was 0.769, indicating a significant positive correlation (p < 0.001). Conclusion: In this study, the use of a new PACT for calculating TEE and PAL was evaluated as valid. A web version of the software program and a smartphone application need to be developed using PACT to make it easier to apply for research purposes.

• Journal of Korean Biological Nursing Science
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• v.25 no.1
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• pp.20-31
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• 2023

Purpose: This study aimed to determine the consistency of 1-day and 3-day average dietary intake using the 24-hour diet recall method and to investigate the relationship of diet intake with physiological indicators potentially associated with diabetic complications in patients with diabetes. Methods: This study conducted a secondary data analysis using pretest data of a nursing intervention study entitled "Development of deep learning based AI coaching program for diabetic patients with high risk and examination of its effects." Data were analyzed through descriptive analysis, one-way repeated-measures analysis of variance, and Pearson correlation coefficients using SPSS 26.0. Results: The average total daily calorie intake over 3 days was 1,494.48 ± 436.47 kcal/day: 1,510.90 ± 547.76 kcal/day on the first day, 1,414.22 ± 527.58 kcal/day on the second day, 1,558.34 ± 645.83 kcal/ day on the third day, showing significant differences (F = 3.59, p = .031). The correlation coefficient between the 1-day and 3-day average dietary intake was 0.41-0.77 for each nutrient and 0.62-0.80 for each food group. Vegetable intake showed negative correlations with body mass index (BMI; r = -.19, p = .023) and triglycerides (r = -.18, p = .036), whereas dairy intake was positively associated with low-density lipoprotein-cholesterol (LDL; r = -0.18, p = .034) and triglycerides (r = .40, p<.001). Conclusion: This study demonstrated that 1-day dietary intake was highly correlated with 3-day average dietary intake using the 24-hour diet recall method. Food groups showed significant associations with physiological indicators of potential diabetic complications such as BMI, triglycerides, and LDL levels. Further studies are needed to improve the knowledge base on the relationships between physiological indicators and food groups.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• Explosives and Blasting
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• v.41 no.3
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• pp.26-37
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• 2023

In order to determine the amount of explosives that can minimize the vibration generated during tunnel construction using the blasting method, it is necessary to derive the blasting vibration coefficients, K and n, by analyzing the vibration records of trial blasting in the field or under similar conditions. In this study, we aimed to develop a technique that can derive reasonable K and n when trial blasting cannot be performed. To this end, we collected full-scale trial blast data and studied how to predict the blast vibration coefficient (K, n) according to the type of explosive, center cut blasting method, rock origin and type, and rock grade using deep learning (DL). In addition, the correction value between full-scale and borehole trial blasting results was calculated to compensate for the limitations of the borehole trial blasting results and to carry out a design that aligns more closely with reality. In this study, when comparing the available explosive amount according to the borehole trial blasting result equation, the predictions from deep learning (DL) exceed 50%, and the result with the correction value is similar to other blast vibration estimation equations or about 20% more, enabling more economical design.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.12
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• pp.1311-1320
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• 2005

The purpose of this research is to survey characteristics of microbial community and the removal efficiency of organic materials for biological activated carbon in water treatment plant. Coal based activated carbon retained more attached bacterial biomass on the surface of the activated carbon than the other activated carbon with operating time and materials. The heterotrophic plate count(HPC), eubacteria(EUB) and 4,6-diamidino-2-phenylindole(DAPI) counts were ranged from $0.95{\times}10^7$ to $52.4{\times}10^7$ CFU/g, from $3.8{\times}10^8$ to $134.2{\times}10^8$ cells/g and from $7.0{\times}10^8$ to $250.2{\times}10^8$ cells/g, respectively. The biomass of EUB and DAPI appeared to be much more $10^2$ than HPC, which were increasing in bed volume of 20,000 at the stage of steady-state. The change of microbial community by analyzing fluorescent in situ hybridization(FISH) method with rRNA-targeted oligonucleotide probes, the dominant group was $\alpha$-proteobacteria($\alpha$ group) and high G+C content bacteria(HGC) the lowest distributing rate before reaching the bed volume of 20,000. After reaching the bed volume of 20,000, $\alpha$ group and other groups of bacteria became decreased, on the other hand, the proportion of both $\beta$-proteobacteria($\beta$ group) and $\gamma$-proteobacteri($\gamma$ group) were increasing. Coconut and wood based activated carbons had similar trend with coal based activated carbon, but the rate of $\alpha$ group on coal based activated carbon had gradually increased. Bacterial production with the operating period appeared highest in coal based activated carbon at the range of $1.2{\sim}3.4\;mg-C/m^3{\cdot}h$ while the coconut and wood based activated carbon were ranged from 1.1 to 2.6 $mg-C/m^3{\cdot}h$ and from 0.7 to 3.5 $mg-C/m^3{\cdot}h$ respectively. The removal efficiency of assimilable organic carbon(AOC) showed to be highly correlated with bacterial production. The correlation coefficient between removal efficiency of AOC and bacterial production were 0.679 at wood based activated carbon, 0.291 at coconut based activated carbon and 0.762 at coal based activated carbon, respectively.