• Korean Journal of Food Science and Technology
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• v.46 no.5
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• pp.641-647
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• 2014

In this study, we evaluated the effect of Capsosiphon fulvescens extract (CFE) and its active compound, pheophorbide A (PhA), on diabetic kidney failure. Diabetes mellitus (DM) was induced by a single intraperitoneal injection of streptozotocin (STZ; 40 mg/kg body weight (BW)). After a week, the rats were orally administered CFE (4 and 20 mg/kg BW) or PhA (0.2 mg/kg BW) once a day for 9 weeks. After scarification, renal tissue samples were collected for biochemical and histochemical analyses. Our study showed that the treatment with CFE and PhA significantly decreased lipid peroxidation level and the activities of glutathione peroxidase and glutathione-S-transferase (p<0.05), but it increased glutathione level and the activities of glutathione reductase, superoxide dismutase, and catalase in the renal tissues (p<0.05). The CFE- and PhA-treated rats with DM showed improved histochemical appearance and decreased abnormal glycogen accumulation. Therefore, we suggest that PhA-containing CFE could exert renal protective effects against STZ-induced oxidative stress.

• Journal of the Korean Society of Food Science and Nutrition
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• v.26 no.4
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• pp.704-713
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• 1997

This study was conducted to describe qualitatively the protein metabolism of pigs during growth depending on the feed protein quality and to describe quantitatively amino acids requirements, using a simulation model. The used model has a non-linear structure. In the used model, the protein utilization system of a pig, which is in the non-steady-state, is described with 15 flux equations and 11 differential equations and is composed with two compartments. Protein deposition(g/day) of pigs on the 30th, 60th, 90th, and 120th day of feeding duration with three-quality protein, beginning with body weight 20kg, were calculated according to the empirical model, PAF(the product of amino acid functions) of Menke, and was used as object function for the simulation. The mean of relative difference between the simulated protein deposition and PAF calculated values, lied in a range of 8.8%. The simulated protein deposition showed different behavior according to feed protein quality. In the high-quality protein, it showed paraboloidal form with extending growth simulation up to 150eh day. So the maximum of protein deposition was acquired on the 105th day of simulate growth time and then it decreased fast. In the low-quality protein, this form of protein deposition in the course of simulated growth did not appear until 150th day. The simulated protein mass also showed a difference in accordance with feed protein quality. The difference was small on the 30th day of simulated growth, but with duration of the simulated growth it was larger. On the 150th day the simulated protein deposition of high quality protein was 1.5 times higher as compared to the low-quality protein. The simulated protein synthesis and break-down rates(g/day) in the whole body showed a parallel behavior in the course of growth, according to feed protein quality. It was found that the improvement of feed protein quality increased protein deposition in the whole body through a increase of both protein synthesis and breakdown during growth. Also protein deposition efficiency, which was calculated from simulated protein deposition and protein synthesis, showed a difference in dependence on the protein qualify of feed protein. The protein deposition efficiency was higher in pigs fed with high quality protein, especially at the simulation time 30th day. But this phenomena disappeared with growth, so on the 150th day of growth, the protein deposition of the high feed protein quality was lowest among the three different quality of feed protein. The simulated total requirement of the 10 essential amino acids for the growth of pigs was 28.1(g/100g protein), similar to NRC. The requirement of lysine was 4.2(g/100g protein).

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.2
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• pp.72-87
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• 2022

For the sustainable development and conservation of the national land, planned development and efficient environmental conservation must be accompanied. To this end, it is possible to induce development and conservation to harmonize by deriving factors affecting development through analysis of previously developed areas and applying appropriate management measures to areas with high development pressure. In this study, the relationship between the area where the land cover changed from forest to urbanization and various social, geographical, and restrictive factors was implemented in a regression formula through logistic regression analysis, and potential development sites were analyzed for Yongin City. The factor that has the greatest impact on the analysis of potential development area is the restrict factors such as Green Belt and protected areas, and the factor with the least impact is the population density. About 148km²(52%) of Yongin-si's forests were analyzed as potential development area. Among the potential development sites, the area with excellent environmental value as a protected area and 1st grade on the Environment Conservation Value Assessment Map was derived as about 13km². Protected areas with high development potential were riparian buffer zone and special measurement area, and areas with excellent natural scenery and river were preferred as development areas. Protected areas allow certain actions to protect individual property rights. However, there is no clear permit criteria, and the environmental impact of permits is not understood. This is identified as a factor that prevents protected areas from functioning properly. Therefore, it needs to be managed through clear exception permit criteria and environmental impact monitoring.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.327-339
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• 2022

The purpose of this study is to present the effective smoke control flow rate and mode for securing safety through quantitative risk assessment according to the smoke control flow rate and mode (supply or exhaust) of the platform when a train fire occurs at the subway platform. To this end, a fire outbreak scenario was created using a side platform with a central staircase as a model and fire analysis was performed for each scenario to compare and analyze fire propagation characteristics and ASET, evacuation analysis was performed to predict the number of deaths. In addition, a fire accident rate (F)/number of deaths (N) diagram (F/N diagram) was prepared for each scenario to compare and evaluate the risk according to the smoke control flow rate and mode. In the ASET analysis of harmful factors, carbon monoxide, temperature, and visible distance determined by performance-oriented design methods and standards for firefighting facilities, the effect of visible distance is the largest, In the case where the delay in entering the platform of the fire train was not taken into account, the ASET was analyzed to be about 800 seconds when the air flow rate was 4 × 833 m³/min. The estimated number of deaths varies greatly depending on the location of the vehicle of fire train, In the case of a fire occurring in a vehicle adjacent to the stairs, it is shown that the increase is up to three times that of the vehicle in the lead. In addition, when the smoke control flow rate increases, the number of fatalities decreases, and the reduction rate of the air supply method rather than the exhaust method increases. When the supply flow rate is 4 × 833 m³/min, the expected number of deaths is reduced to 13% compared to the case where ventilation is not performed. As a result of the risk assessment, it is found that the current social risk assessment criteria are satisfied when smoke control is performed, and the number of deaths is the flow rate 4 × 833 m³/min when smoke control is performed at 29.9 people in 10,000 year, It was analyzed that it decreased to 4.36 people.