• The Journal of Industrial Distribution & Business
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• v.2 no.1
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• pp.5-15
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• 2011

Prior to the introduction of the CRM, we need to analyze the characteristics and the situations of the company, and should establish a clear vision of the CRM. And each company should identify elements and technologies for introducing the most suitable CRM for them, and optimize them, with long-term perspective. In addition, it requires the implementation strategy which integrates the existing company's routine marketing activities with the concept of the CRM. According to the implementation strategy, the company should improve the business process which is the most effective in investment step by step, and the information system strategy, which develops system investment gradually, should harmonize with it. First, we recognized that raising the company value is important by maximizing customer lifetime value (LTV) by understanding customer needs, and achieving the company's goal through customer satisfaction. Second, we understood that adopting the CRM should be accompanied by changes in the structure, business process and customer contact channels, and it can be successfully integrated with business when it gets proper understandings and attentions of the management. Third, the reality is that there are few cases of successful implementation of domestic companies, and some companies that successfully implement the system mean nothing but implement the solution for developing the CRM. Therefore, it needs to be observed for the long haul, and it seems that we need to approach more systematically to implementation cases for each industry about implementation of the CRM. Fourth, the CRM is no longer the preserve of major companies, and it is the time that medium and small sized enterprises also need it. Taking lesson from Switzerland's small size store merchants who successfully adopt right size of the CRM for their business, for domestic medium and small sized enterprises, the necessity to develop business through developing the CRM models which fit their situations and maintaining relationships with customers has been grown. Fifth, for adopting the CRM business processes, changing or converting the CRM system to the model which fits the company's situation is important rather than applying the advanced company's CRM system model. In other words, the CRM solution which can maximize their own strength by developing the CRM program that makes the most of features and characteristics of the company should be adopted.

• Korean Journal of Environmental Biology
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• v.37 no.4
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• pp.568-578
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• 2019

The nymphal development of the potato aphid, Macrosiphum euphorbiae (Thomas), was studied at seven constant temperatures (12.5, 15.0, 17.5, 20.0, 22.5, 25.0, and 27.5±1℃), 65±5% relative humidity (RH), and 16:8 h light/dark photoperiods. The developmental investigation of M. euphorbiae was separated into two steps, the 1^st through 2^nd and the 3^rd through 4^th stages. The mortality was under 10% at six temperatures. However, it was 53.0% at 27.5℃. The developmental time of the entire nymph stage was 15.5 days at 15.0℃, 6.7 days at 25.0℃, and 9.7 days at 27.5℃. In the immature stage, the lower threshold temperature of the larvae was 2.6℃ and the thermal constant was 144.5 DD. In our analysis of the temperature-development experiment, the Logan-6 model equation was most appropriate for the non-linear regression models (r²=0.99). When the distribution completion model of each development stage of M. euphorbiae larvae was applied to the 2-parameter and 3-parameter Weibull functions, each of the model's goodness of fit was very similar (r²=0.92 and 0.93, respectively). The adult longevity decreased as the temperature increased but the total fecundity of the females at each temperature was highest at 20℃. The life table parameters were calculated using the whole lifespan periods of M. euphorbiae at the above six temperatures. The net reproduction rate (R₀) was highest at 20.0℃(63.2). The intrinsic rate of increase (r_m) was highest at 25℃(1.393). The finite rate of doubling time (D_t) was the shortest at 25.0℃(2.091). The finite rate of increase (λ) was also the highest at 25.0℃(1.393). The mean generation time(T) was the shortest at 25.0℃(9.929).

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.11
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• pp.1700-1707
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• 2015

The effects of drying temperature on drying characteristics of soybeans with different grain sizes [6.0 (S), 7.5 (M), and 9.0 mm (L) (${\pm}0.2$)] with 25.0% (${\pm}0.8$) initial moisture content were studied. Drying temperatures varied at 25, 35, and $45^{\circ}C$, with a constant air velocity (13.2 m/s). Thin-layer drying models were applied to describe the drying process of soybeans. The Midilli-Kucuk model showed the best fit ($R^2$ >0.99). Based on the model parameters, drying time to achieve the target moisture content (10%) was successfully estimated. Drying time was strongly dependent on the size of soybeans and the drying temperature. The effective moisture diffusivity ($D_{eff}$) was estimated by the diffusion model based on Fick's second law. $D_{eff}$ values increased as grain size and drying temperature increased due to the combined effect of high temperatures and high drying rates, which promote compact tissue. Deff values of S, M, and L estimated were in the range of $0.83{\times}10^{-10}$ to $1.51{\times}10^{-10}m^2/s$, $1.17{\times}10^{-10}$ to $2.17{\times}10^{-10}m^2/s$, and $1.53{\times}10^{-10}$ to $2.95{\times}10^{-10}m^2/s$, respectively, whereas activation energy ($E_a$) based on drying temperature showed no significant differences in the size of soybeans.

• Journal of Radiation Protection and Research
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• v.40 no.1
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• pp.10-16
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• 2015

Bone changes are common sequela of irradiation in growing animal. The purpose of this study was to establish an experimental model of radiation-induced bone loss in growing mice using micro-computed tomography (${\mu}CT$). The extent of changes following 2 Gy gamma irradiation ($2Gy{\cdot}min^{-1}$) was studied at 4, 8 or 12 weeks after exposure. Mice that received 0.5, 1.0, 2.0 or 4.0 Gy of gamma-rays were examined 8 weeks after irradiation. Tibiae were analyzed using ${\mu}CT$. Serum alkaline phosphatase (ALP) and biomechanical properties were measured and the osteoclast surface was examined. A significant loss of trabecular bone in tibiae was evident 8 weeks after exposure. Measurements performed after irradiation showed a dose-related decrease in trabecular bone volume fraction (BV/TV) and bone mineral density (BMD), respectively. The best-fitting dose-response curves were linear-quadratic. Taking the controls into accounts, the lines of best fit were as follows: BV/TV (%) = $0.9584D^2-6.0168D+20.377$ ($r^2$ = 0.946, D = dose in Gy) and BMD ($mg{\cdot}cm^{-3}$) = $8.8115D^2-56.197D+194.41$ ($r^2$ = 0.999, D = dose in Gy). Body weight did not differ among the groups. No dose-dependent differences were apparent among the groups with regard to mechanical and anatomical properties of tibia, serum ALP and osteoclast activity. The findings provide the basis required for better understanding of the results that will be obtained in any further studies of radiation-induced bone responses.

• Journal of Wetlands Research
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• v.17 no.2
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• pp.193-202
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• 2015

In this study, a long term monitering of nonpoint source pollution runoff is conducted at the area of transportation related and EMCs(Event Mean Concentrations) in terms of water quality items, such as BOD, $COD_{Mn}$, SS, T-N and T-P are determined for each not only runoff event and but also observation site. On the other hands, SWMM(Storm Water Management Model) model is constructed using the data collected in the transportation areas selected. Model calibration and verification of SWMM is carried out based on the data collected. And simulated EMCs was compared with observed EMCs by monitoring and prior studies. SWMM applicability estimation was Using the compared result. The results of simulation showed that BOD 5.787 ~ 14.475 mg/L, $COD_{Mn}$ 12.946 ~ 59.611 mg/L, SS 13.742 ~ 46.208 mg/L, T-N 2.037 ~ 5.213 mg/L, T-P 0.117 ~ 0.415 mg/L. And a differential between simulated EMCs and observed EMCs is too low so comparing result show high fit(BOD 4.27 %, $COD_{Mn}$ 4.87%, SS 2.31%, T-N 5.78%, T-P 14.45%). A results of compared with the prior studies, BOD and T-P are included range of prior studies, $COD_{Mn}$ and SS are lower than range of prior studies, T-N is higher than range of prior studies. Differential between simulated EMCs and prior studies EMCs was showing for survey seasonal and changing land-use, so from now on, EMCs of using the internal representatives value will be calculated by more monitoring toward various precipitation events.

• Horticultural Science & Technology
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• v.31 no.5
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• pp.531-537
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• 2013

The average annual and winter ambient air temperatures in Korea have risen by 0.7 and $1.4^{\circ}C$, respectively, during the last 30 years. Radish (Raphanus sativus), one of the most important cool season crops, may well be used as a model to study the influence of climatic change on plant growth, because it is more adversely affected by elevated temperatures than warm season crops. This study examined the influence of transplanting time, nitrogen fertilizer level, and climate parameters, including air temperature and growing degree days (GDD), on the performance of a radish cultivar 'Mansahyungtong' to estimate crop growth during the spring growing season. The radish seeds were sown from April 24 to May 22, 2012, at internals of 14 days and cultivated with 3 levels of nitrogen fertilization. The data from plants sown on April 24 and May 8, 2012 were used for the prediction of plant growth as affected by planting date and nitrogen fertilization for spring production. In our study, plant fresh weight was higher when the radish seeds were sown on $24^{th}$ of April than on $8^{th}$ and $22^{nd}$ of May. The growth model was described as a logarithmic function using GDD according to the nitrogen fertilization levels: for 0.5N, root dry matter = 84.66/(1+exp (-(GDD - 790.7)/122.3)) ($r^2$ = 0.92), for 1.0N, root dry matter = 100.6/(1 + exp (-(GDD - 824.8)/112.8)) ($r^2$ = 0.92), and for 2.0N, root dry matter = 117.7/(1+exp (-(GDD - 877.7)/148.5)) ($r^2$ = 0.94). Although the model slightly tended to overestimate the dry mass per plant, the estimated and observed root dry matter and top dry matter data showed a reasonable good fit with 1.12 ($R^2$ = 0.979) and 1.05 ($R^2$ = 0.991), respectively. Results of this study suggest that the GDD values can be used as a good indicator in predicting the root growth of radish.

• Ultrasonography
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• v.32 no.2
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• pp.132-142
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• 2013

Purpose: The purpose of this study is to investigate the correlations of various kinetic parameters derived from the time intensity curve in a xenograft mouse model injected with a prostate cancer model (PC-3 and LNCaP) using an ultrasound contrast agent with histopathologic parameters. Materials and Methods: Twenty nude mice were injected with human prostate cancer cells (15 PC-3 and five LNCaP) on their hind limbs. A bolus of $500{\mu}L$ ($1{\times}10^8$ microbubbles) of second-generation US contrast agent (SonoVue) was injected into the retroorbital vein. The region of interest was drawn over the entire tumor. The time intensity curve was acquired and then fitted to a gamma variate function. The maximal intensity (A), time to peak (Tp), maximal wash-in rate (washin), washout rate (washout), area under the curve up to 50 sec ($AUC_{50}$), area under the ascending slope ($AUC_{in}$), and area under the descending slope ($AUC_{out}$) were derived from the parameters of the gamma variate fit. Immunohistochemical staining for VEGF and CD31 was performed. Tumor volume, the area percentage of VEGF stained in a field, and the count of CD31 (microvessel density, MVD) positive vessels showed correlation with the parameters from the time intensity curve. Results: No significant differences were observed between the kinetic and histopathological parameters from each group. MVD showed positive correlation with A (r=0.625, p=0.003), washin (r=0.462, p=0.040), $AUC_{50}$ (r=0.604, p=0.005), and $AUC_{out}$ (r=0.587, p=0.007). Positive correlations were also observed between tumor volume and $AUC_{50}$ (r=0.481, p=0.032), washin (r=0.662, p=0.001), and $AUC_{out}$ (r=0.547, p=0.012). Washout showed negative correlations with MVD (r=-0.454, p=0.044) and tumor volume (r=-0.464, p=0.039). The area percentage of VEGF did not show any correlation with calculated data from the curve. Conclusion: MVD showed correlations with several of the kinetic parameters. CEUS has the potential for prediction of tumor vascularity in a prostate cancer animal model.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Journal of The Korean Association For Science Education
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• v.42 no.3
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• pp.371-381
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• 2022

This study causally investigates whether high school student with high science learning motivation becomes to achieve more or vice versa, and also how those two factors affect STEM career motivation. Research participants were 1st year students in a high school at Seoul. We surveyed their science learning motivation three times in the same time interval in the fall semester of 2021, and once a STEM career motivation in the third period. We collected data from 171 students with their mid-term and final exam scores, with which, we constructed and fitted an autoregressive cross-lagged model. The research model shows high measurement stability and fit indices. All the autoregressive and cross-lagged paths were statistically significant. However, standardized regression coefficients were larger in path from motivation to achievement compared to the opposite. Only science learning motivation shows significant direct effect on STEM career motivation, rather than achievement. For indirect effects, the first science learning motivation affected the final exam score and STEM career motivation, and the final exam score affected STEM career motivation. However, the final exam score did not have a total effect toward STEM career motivation. The result of this study shows reciprocal and cyclic causality between science learning motivation and achievement - in comparison, the effect of motivation for the opposite is larger than that of achievement. Also the result of this study strongly reaffirms the importance of science learning motivation. Instructional implications for strengthening science learning motivation throughout a semester was discussed, and a study for the longitudinal effect of science learning motivation and achievement in high school student toward future STEM vocational life was suggested.