• Journal of Gastric Cancer
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• v.21 no.3
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• pp.268-278
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• 2021

Purpose: While several prognostic models for the stratification of death risk have been developed for patients with advanced gastric cancer receiving first-line chemotherapy, they have seldom been tested in the Chinese population. This study investigated the performance of these models and identified the optimal tools for Chinese patients. Materials and Methods: Patients diagnosed with metastatic or recurrent gastric adenocarcinoma who received first-line chemotherapy were eligible for inclusion in the validation cohort. Their clinical data and survival outcomes were retrieved and documented. Time-dependent receiver operating characteristic (ROC) and calibration curves were used to evaluate the predictive ability of the models. Kaplan-Meier curves were plotted for patients in different risk groups divided by 7 published stratification tools. Log-rank tests with pairwise comparisons were used to compare survival differences. Results: The analysis included a total of 346 patients with metastatic or recurrent disease. The median overall survival time was 11.9 months. The patients were different into different risk groups according to the prognostic stratification models, which showed variability in distinguishing mortality risk in these patients. The model proposed by Kim et al. showed relative higher predicting abilities compared to the other models, with the highest χ² (25.8) value in log-rank tests across subgroups, and areas under the curve values at 6, 12, and 24 months of 0.65 (95% confidence interval [CI]: 0.59-0.72), 0.60 (0.54-0.65), and 0.63 (0.56-0.69), respectively. Conclusions: Among existing prognostic tools, the models constructed by Kim et al., which incorporated performance status score, neutrophil-to-lymphocyte ratio, alkaline phosphatase, albumin, and tumor differentiation, were more effective in stratifying Chinese patients with gastric cancer receiving first-line chemotherapy.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.48-59
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• 2021

In this study, experiments were conducted to investigate the compressive·flexural performances of single fiber-reinforced mortar (FRM) using only steel fiber or carbon fiber which has different material properties as well as hybrid FRM using a mixture of steel and carbon fibers. The mortar specimens incorporated steel and carbon fibers in the mix proportions of 1+0%, 0.75+0.25%, 0.5+0.5%, 0.25+0.75% and 0+1% by volume at a total volume fraction of 1.0%. Their mechanical performance was compared and examined with a plain mortar without fiber at 28 days of age. The experiments of mortar showed that the hybrid FRM using a mixture of 0.75% steel fibers + 0.25% carbon fibers had the highest compressive and flexural strength, confirming by thus the synergistic reinforcing effect of the hybrid FRM. On the contrast, in the case of hybrid FRM using a mixture of 0.5% steel fibers + 0.5% carbon fibers witnessed the highest flexural toughness, suggesting as a result the optimal fiber mixing ratio of hybrid FRM to improve the strength and flexural toughness at the same time. Moreover, the fracture surface was observed through a scanning electron microscope (SEM) for image analysis of the FRM specimen. These results were of great help for images analysis of hybrid reinforcing fibers in cement matrix.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.30-35
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• 2021

The present study examined the effects of various guide fin structures and boundary parameters on the thermal performance of heat exchangers used in heat recovery thermoelectric generators. The heat transfer rate and pressure drop of the heat exchangers without fins, with circular fins, with triangular fins, and with combined circular and triangular fins were simulated numerically using ANSYS 19.1 commercial code to confirm the effect of the guide fin structures. The heat transfer rate of the heat exchanger with combined fins was 27.0%, 5.2%, and 1.5% higher than those without fins, with circular fins, and with triangular fins, respectively. The pressure drop characteristic of the heat exchanger with the combined fins was 28.3% higher than that without fins but 9.2% and 10.5% lower than those with circular fins and with triangular fins, respectively. The heat exchanger with combined fins as the optimal model showed the highest heat transfer rate of 5664.9 W and pressure drop of 1454.02 Pa for highest hot gas temperature, maximum flow rates of hot gas and coolant, and lowest coolant temperature.

• Journal of Sensor Science and Technology
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• v.30 no.3
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• pp.154-164
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• 2021

In this study, the IoT sensor technology required for improving the survival rate and high-density productivity of individual shrimp in smart shrimp farming (which involves the usage of recirculating aquaculture systems and biofloc technology) was analyzed. The principles and performances of domestic and overseas water quality monitoring IoT sensors were compared. Furthermore, the drawbacks of existing aquaculture monitoring technologies and the countermeasures for future aquaculture monitoring technologies were examined. In particular, for farming white-legged shrimp, an IoT sensor was employed to collect measurement indicators for managing the water quality environment in real-time, and the IoT sensor-based real-time monitoring technology was then analyzed for implementing the optimal farming environment. The results obtained from this study can potentially contribute to the realization of an autonomous farming platform that can improve the survival rate and productivity of shrimp, achieve feed reduction, improve the water quality environment, and save energy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.203-213
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• 2021

A vehicle crash occurs due to various factors such as the geometry of the road section, traffic, and driver characteristics. A safety performance function has been used in many studies to estimate the relationship between vehicle crash and road factors statistically. And depends on the purpose of the analysis, various characteristic variables have been used. And various characteristic variables have been used in the studies depending on the purpose of analysis. The existing domestic studies generally reflect the average characteristics of the sections by quantifying the traffic volume in macro aggregate units such as the ADT, but this has a limitation that it cannot reflect the real-time changing traffic characteristics. Therefore, the need for research on effective aggregation units that can flexibly reflect the characteristics of the traffic environment arises. In this paper, we develop a safety performance function that can reflect the traffic characteristics in detail with an aggregate unit for one hour in addition to the daily model used in the previous studies. As part of the present study, we also perform a comparison and evaluation between models. The safety performance function for daily and hourly units is developed using a negative binomial regression model with the number of accidents as a dependent variable. In addition, the optimal negative binomial regression model for each of the hourly and daily models was selected, and their prediction performances were compared. The model and evaluation results presented in this paper can be used to determine the risk factors for accidents in the highway section considering the dynamic characteristics. In addition, the model and evaluation results can also be used as the basis for evaluating the availability and transferability of the hourly model.

• The Korean Journal of Franchise Management
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• v.13 no.3
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• pp.17-30
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• 2022

Purpose: Recent emergence of diverse businesses in the distribution industry has led small and medium-sized retailers and their distribution logistics centers to face difficulties. Transactions between companies are connected within a supply chain, and the companies have relationships in the form of a supplier and a buyer. Therefore, it is important to identify causes of problems among companies through supply chain and strategic partnerships, thus developing optimal management plans and maximizing performances of companies. This study proposes that sustainable supply chain management consists of product quality, price quality, distribution quality, and promotion quality based on stakeholder theory and resource-based view. This study examined the impacts of sustainable chain management factors on satisfaction and win-win cooperation. Research design, data, and methodology: In the proposed model, satisfaction plays a mediating role in the relationship between sustainable chain management and win-win cooperation. The data were collected from 245 owners who use small and medium-sized distribution logistics center and analyzed using 2SLS (two-stage least square) with SPSS 28.0. Exploratory factor analysis and correlation analysis were used to assess the validity and reliability of constructs. Results: The findings are as follows. In the case of the total and Nadeulgage samples, product, price, and distribution quality had a significant positive effect on satisfaction, but in the case of Neighborhood super, product and price quality have a significant positive effect on satisfaction. Satisfaction has a significant positive effect on win-win cooperation in the overall, Nadeulgage, and Neighborhood super. Satisfaction plays a partial or full mediating role in the case of total, Nadeulgage, Neighborhood super. Conclusions: This study emphasized the need for sustainable supply chain management of small and medium-sized distribution logistics centers by examining the relationship between small and medium-sized distribution logistics centers and chain stores. It was found that store satisfaction plays an important role in the win-win cooperation between small and medium-sized distribution logistics centers and chain stores. Small and medium-sized distribution logistics centers can maximize product quality, price quality, distribution quality, and promotion quality by understanding the effect of chain store-related satisfaction and win-win cooperation on chain stores.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.482-489
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• 2022

This study aimed to determine the optimal planting density of Fraxinus rhynchophylla assessed from the early growth performance at various planting densities over the 7-year period after planting. The study site was in Pyeongchang County, South Korea, and seedlings of 2-year-old (bare-root seedlings) F. rhynchophylla were planted at four densities (3,000, 5,000, 7,000, and 10,000 trees ha^-1) in March 2015. The survival rate, root-collar diameter (RCD), and height (H) were measured from 2015 to 2021, and the H/D (H/RCD) ratio and stem volume were calculated. The survival rate (84-97%) and H/D ratio (54.5-59.2%) were not affected by the planting density during the study period, but the RCD, H, and stem volume were significantly higher for 7,000 trees ha^-1 than for other planting densities. Especially, the stem volume (cm³ tree^-1) at 7 years after planting was highest for 7,000 trees ha^-1 (1,356.1), followed by 10,000 trees ha^-1 (958.6), 5,000 trees ha^-1 (773.0), and 3,000 trees ha^-1 (579.5). As the planting density increased, F. rhynchophylla seedlings showed initial rapid growth due to light competition, but relatively low growth at excessive planting densities. In the future, use of a suitable planting density considering planting costs should provide outstanding growth performance of F. rhynchophylla on plantations.

• Knowledge Management Research
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• v.23 no.1
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• pp.89-109
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• 2022

Due to COVID-19 and soaring participation of individual investors, large-scale transactions exceeding system capacity limits have been reported frequently in the capital market. The capital market IT systems, which the impact of system failure is very critical, have encountered unexpectedly tremendous transactions in 2020, resulting in a sharp increase in system failures. Despite the fact that many companies maintained large-scale system capacity planning policies, recent transaction influx suggests that a new approach to capacity planning is required. Therefore, this study developed capital market IT system capacity planning models using machine learning techniques and analyzed those performances. In addition, the performance of the best proposed model was improved by using sentiment index that can promptly reflect the behavior of investors. The model uses empirical data including the COVID-19 period, and has high performance and stability that can be used in practice. In practical significance, this study maximizes the cost-efficiency of a company, but also presents optimal parameters in consideration of the practical constraints involved in changing the system. Additionally, by proving that the sentiment index can be used as a major variable in system capacity planning, it shows that the sentiment index can be actively used for various other forecasting demands.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.140-161
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• 2022

Seismic data with missing traces are often obtained regularly or irregularly due to environmental and economic constraints in their acquisition. Accordingly, seismic data interpolation is an essential step in seismic data processing. Recently, research activity on machine learning-based seismic data interpolation has been flourishing. In particular, convolutional neural network (CNN) and generative adversarial network (GAN), which are widely used algorithms for super-resolution problem solving in the image processing field, are also used for seismic data interpolation. In this study, CNN-based algorithm, U-Net and GAN-based algorithm, and conditional Wasserstein GAN (cWGAN) were used as seismic data interpolation methods. The results and performances of the methods were evaluated thoroughly to find an optimal interpolation method, which reconstructs with high accuracy missing seismic data. The work process for model training and performance evaluation was divided into two cases (i.e., Cases I and II). In Case I, we trained the model using only the regularly sampled data with 50% missing traces. We evaluated the model performance by applying the trained model to a total of six different test datasets, which consisted of a combination of regular, irregular, and sampling ratios. In Case II, six different models were generated using the training datasets sampled in the same way as the six test datasets. The models were applied to the same test datasets used in Case I to compare the results. We found that cWGAN showed better prediction performance than U-Net with higher PSNR and SSIM. However, cWGAN generated additional noise to the prediction results; thus, an ensemble technique was performed to remove the noise and improve the accuracy. The cWGAN ensemble model removed successfully the noise and showed improved PSNR and SSIM compared with existing individual models.

• Korean Journal of Environmental Biology
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• v.40 no.4
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• pp.651-659
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• 2022

This study was conducted to investigate the effects of different water temperatures (8, 11, 14 and 17℃) on growth, survival and hematological parameters of juvenile chum salmon(Oncorhynchus keta) for eight weeks. At the end of the experiment, at 14℃, the final body weights of the O. keta group were the highest compared to the other groups. Also, the O. keta showed a higher tendency in the 14℃ group than the 8, 11, and 17℃ groups in terms of growth performances, including specific growth rate (SGR), feed conversion ratio (FCR), feed efficiency (FE), weight gain (WG), and condition factor (CF). The survival rate (SR) was 100% at 8 and 11℃ groups, 96% at 14℃ group and 98% at 17℃ group. In the plasma components, the alanine aminotransferase (ALT) was significantly decreased at 17℃ group, whereas there was no significant change in the albumin (ALB), total protein (TP), sodium (Na⁺), potassium (K⁺) and chloride (Cl^-) levels. Among the whole-body composition of salmon, moisture, crude protein, and ash were not significantly affected by water temperature. However, crude lipid in the 8℃ group was significantly higher than in other water temperature groups. The results of this study demonstrated that the optimal temperature to stable growth performance for juvenile O. keta was 14℃.