• Nuclear Engineering and Technology
• /
• v.33 no.2
• /
• pp.209-224
• /
• 2001

The SSC-K system analysis code is under development at the Korea Atomic Energy Research Institute (KAERI) as a part of the KALIMER project. The SSC-K code is being used as the principal tool for analyzing a variety of off-normal conditions or accidents of the preliminary KALIMER design. The SSC-K code features a multiple-channel core representation coupled with a point kinetics model with reactivity feedback. It provides a detailed, one-dimensional thermal-hydraulic simulation of the primary and secondary sodium coolant circuits, as well as the balance-of-plant steam/water circuit. Recently a two-dimensional hot pool model was incorporated into SSC-K for analysis of thermal stratification phenomena in the hot pool. In addition, SSC-K contains detailed models for the passive decay heat removal system and a generalized plant control system. The SSC-K code has also been applied to the computational engine for an interactive simulation of the KALIMER plant. This paper presents an overview of the recent activities concerned with SSC-K code model development This paper focuses on both descriptions of the newly adopted thermal hydraulic and neutronic models, and applications to KALIMER analyses for typical anticipated transients without scram.

• Journal of Biosystems Engineering
• /
• v.38 no.3
• /
• pp.185-191
• /
• 2013

Purpose: The present study focused on the estimation of soluble solids content (SSC) of chestnut using reflectance and transmittance spectra in range of VIS/NIR. Methods: Four species intact/peeled chestnuts were used for acquisition of spectral data. Transmittance and reflectance spectra were used to develop the best PLS model to estimate SSC of chestnut. Results: The model developed with the transmitted energy spectra of peeled chestnuts rather than intact chestnuts and with range of NIR rather than VIS performed better. The best $R^2$ and RMSEP of cross validation were represented as 0.54 and $1.85^{\circ}Brix$. The results presented that the reflectance spectra of peeled chestnuts by species showed the best performance to predict SSC of chestnut. $R^2$ and RMSEP were 0.55 and $1.67^{\circ}Brix$. Conclusions: All developed models showed RMSEP around $1.44{\sim}2.54^{\circ}Brix$, which is considered not enough to estimate SSC accurately. It was noted that $R^2$ of cross validation that we found were not high. For all that, grading of the fruits in two or three classes of SSC during postharvest handling seems possible with an inexpensive spectrophotometer. Furthermore, the development of estimation of SSC by each chestnut species could be considered in that SSC distribution is clustering in different range by species.

• Journal of Gifted/Talented Education
• /
• v.20 no.2
• /
• pp.487-502
• /
• 2010

The purpose of this study was to examine the effect of SSC(Small-Scale Chemistry) lab program applying to scientifically gifted students' classes. The participants were 15 the middle school scientifically gifted students selected and enrolled in science-gifted education center of university. SSC lab program was applied for 3 month with 5 topics. The research design was one group pretest-posttest design, the data were analyzed using the PASW 18 statistics program. The results of this study were as follows: First, the scientific attitude of scientifically gifted students was improved significantly(p<.01) after applying SSC lab program. Especially, there were significant difference between pre-test and post-test in the scores of cooperativity, spontaneity, perseverance, which were the sub-region of scientific attitude. Second, the creative personality characteristics of scientifically gifted students showed statistically significant improvements(p<.01) after applying SSC lab program. Especially, there was significant difference between pre-test and post-test in the scores of adhesion, which was a sub-region of the creative personality characteristics. Third, there were no significant differences in the science inquiry skills between before and after applying the SSC lab program in the scientifically gifted students' classes(p>.05). The results suggest that SSC Lab program is an effective program in scientifically gifted students' classes.

• Proceedings of the Korean Magnestics Society Conference
• /
• 2007.12a
• /
• pp.5-5
• /
• 2007

• Asian-Australasian Journal of Animal Sciences
• /
• v.16 no.3
• /
• pp.320-324
• /
• 2003

The objective of this study was to localize QTL affecting meat quality in a pig family of three generations. All animals were genotyped for twenty-four microsatellites on SSC3 (Sus scrofa chromosome 3), SSC4 and SSC7. One hundred and forty $F_2$ offsprings were scored for eleven meat quality traits. Least square regression interval mapping revealed quantitative trait loci (QTL) effect for meat pH (m. Semipinalis Capitis, SC) on SSC4 and SSC7; for moisture (m. Longissimus Dorsi, LD) on SSC3. Furthermore, there was suggestive evidence for a QTL on SSC4 affecting intramuscular fat (IMF) content that nearly approached the chromosomewise (p=0.05) significance threshold.

• Nuclear Engineering and Technology
• /
• v.56 no.3
• /
• pp.819-823
• /
• 2024

The main criteria used in NEI 18-04 to define SSCs as risk-significant include (1) the SSC is required to keep all LBEs within the F-C target, and (2) the total frequency with the SSC failed exceeds 1% of the limit for at least one of the three cumulative risk metrics used for evaluating the integrated plant risk. The first one is a reasonable criterion in determining the risk significant SSCs. However, the second criterion may not be adequate to serve the purpose of determining the risk significance of SSCs. In the second criterion, the cumulative risk metric values representing the integrated plant risk (less the preventive and mitigative effects of the SSC being evaluated) are compared to a risk limit that represents a very small contribution to the overall integrated plant risk, which corresponds appropriately to the contributions from individual SSCs. The easiest approach to redefine the NEI 18-04 definition of risk-significant SSCs in relation to the integrated plant risk metrics is to compare the difference, between the risk metric value calculated with the SSC failed and the risk metric value calculated with the SSC credited, with 1% of the risk limit established for the integrated plant risk metrics.

• Asian-Australasian Journal of Animal Sciences
• /
• v.22 no.2
• /
• pp.187-193
• /
• 2009

The present study identified the favorable conditions for isolation, enrichment and in vitro culture of highly purified, undifferentiated pig spermatogonial stem cell (SSC) lines that proliferate for long periods of time in culture. The colonies displayed morphology similar to miceSSC and were positive for markers of SSC (PGP9.5), proliferating germ cell (PigVASA), pre-meiotic germ cell (DAZL) and pluripotency (OCT4, SSEA-1, NANOG, and SOX2) based on immuno-cytochemistry and RT-PCR. The purity of these colonies was confirmed by negative expression of markers for sertoli cell (GATA4 and SOX9), peritubular myoid cell (${\alpha}$-SMA), differentiating spermatogonial and germ cells (c-KIT). The colonies could be maintained with undifferentiated morphology for more than two months and passaged more than 8 times with doubling time between 6-7 days. Taken together, we conclude that pigSSC could be successfully isolated and cultured in vitro and they possess characteristics similar to miceSSC.

• Journal of Korean Society on Water Environment
• /
• v.37 no.5
• /
• pp.335-343
• /
• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• Proceedings of the Korean Nuclear Society Conference
• /
• 2001.05a
• /
• pp.143.2-143
• /
• 2001

• Journal of Environmental Science International
• /
• v.17 no.9
• /
• pp.981-992
• /
• 2008

This study examined how to produce new methods of copper (II) sulfate crystallization by using a small-scale chemistry tool such as small-scale reaction surface and petri dish. The making of copper(II) sulfate is included in the 5th grade elementary science textbooks. Various copper(II) compounds were reacted with a 2 M sulfuric acid solution. The result of this study is as follows: Seven small amounts of copper(II) compounds were reacted with a few drops of 2 M sulfuric acid solution at room temperature to make a copper(II) sulfate crystal of triclinic shape. Using the petri dish method, a copper(II) sulfate crystal could be identified within one hour of reacting copper(II) hydroxide, copper(II) carbonate, copper(II) nitrate, copper(II) perchlorate, cupric(II) formate from a few drops of 2 M sulfuric acid solution at room temperature. When using the lap top method for copper(II) perchlorate, cupric formate, a proper crystal could be identified within one hour as well. SSC methods were used for the first time to make a copper sulfate crystal via chemical reaction. We can make a copper(II) sulfate crystal using a simple method which is easier, safer and saves time in class. And since a small quantity of chemicals are being used in SSC chemical methods, waste is greatly reduced. This lessens the amount of environmental problems caused by the experiment. This can be helpful in preserving nature. In addition the cost of chemical and laboratory equipment is greatly reduced because it uses material that we find in our daily lives. There will be continued study of small-scale methods such as improvement of new programs, study and training of teachers, and securing SSC tools. I would like to suggest such as SSC methods are applicable in elementary School Science. I would like it to become a wide spread program.