• Horticultural Science & Technology
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• v.30 no.6
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• pp.664-672
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• 2012

As an artificial light source, light-emitting diode (LED) with a short wavelength range can be used in closed-type plant production systems. Among various wavelength ranges in visible light, individual light spectra induce distinguishing influences on plant growth and development. In this study, we determined the effects of monochromatic LEDs on leaf shape index, growth and the accumulation of phytochemicals in a red leaf lettuce (Lactuca sativa L. 'Sunmang') and a green leaf lettuce (Lactuca sativa L. 'Grand rapid TBR'). Lettuce seedlings grown under normal growing conditions ($20^{\circ}C$, fluorescent lamp + high pressure sodium lamp, $130{\pm}5{\mu}mmol{\cdot}m^{-2}{\cdot}s^{-1}$, 12 hours photoperiod) for 18 days were transferred into incubators at $20^{\circ}C$ equipped with various monochromatic LEDs (blue LED, 456 nm; green LED, 518 nm; red LED, 654 nm; white LED, 456 nm + 558 nm) under the same light intensity and photoperiod ($130{\pm}7{\mu}mmol{\cdot}m^{-2}{\cdot}s^{-1}$, 12 hours photoperiod). Leaf length, leaf width, leaf area, fresh and dry weights of shoots and roots, shoot/root ratio, SPAD value, total phenolic concentration, antioxidant capacity, and the expression of a key gene involved in the biosynthesis of phenolic compounds, phenylalanine ammonia-lyase (PAL), were measured at 9 and 23 days after transplanting. The leaf shape indexes of both lettuce cultivars subjected to blue or white LEDs were similar with those of control during whole growth stage. However, red and green LEDs induced significantly higher leaf shape index than the other treatments. The green LED had a negative impact on the lettuce growth. Most of growth characteristics such as fresh and dry weights of shoots and leaf area were the highest in both cultivars subjected to red LED treatment. In case of red leaf lettuce plants, shoot fresh weight under red LED was 3.8 times higher than that under green LED at 23 days after transplanting. In contrast, the accumulation of chlorophyll, phenolics including antioxidants in lettuce plants showed an opposite trend compared with growth. SPAD value, total phenolic concentration, and antioxidant capacity of lettuce grown under blue LED were significantly higher than those under other LED treatments. In addition, PAL gene was remarkably activated by blue LED at 9 days after transplanting. Thus, this study suggested that the light quality using LEDs is a crucial factor for morphology, growth, and phytochemicals of two lettuce cultivars.

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.496-503
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• 2005

This study was conducted to investigate the influences of sapflow flux on soil water tensions and soil moisture content at the Abies holophylla plots in Gwangneung, Gyeonggido, from September to October 2004. The Abies holophylla had been planted in 1976 and thinning and pruning were carried out in 1996 and 2004. Sapflow flux was measured by the heat pulse method, and soil water tension was measured by tensiometer at hillslope and streamside. Time domain reflectometry probes (TDR) were positioned horizontally at the depth of 10, 30 and 50 cm to measure soil moisture content. All of data were recorded every 30 minutes with the dataloggers. The sapflow flux responded sensitively to rainfall, so little sapflow was detected in rainy days. The average daily sapflow flux of sample trees was 10.16l, a maximum was 15.09l, and a minimum was 0.0l. The sapflow flux's diurnal changes showed that sapflow flux increased from 9 am and up to 0.74 l/30 min. The highest sapflow flux maintained by 3 pm and decreased almost 0.0 l/30 mm after 7 pm. The average soil water tensions were low ($-141.3cmH_2O$, $-52.9cmH_2O$ and $-134.2cmH_2O$) at hillslope and high ($-6.1cmH_2O$, $-18.0cmH_2O$ and $-3.7cmH_2O$) at streamside. When the soil moisture content decreased after rainfall, the soil water tension at hillslope responded sensitively to the sapflow flux. The soil water tension decreased as the sapflow flux increased during the day time, whereas increased during the night time when the sapflow flux was not detected. On the other hand, there was no significant relationship between soil water tension and sapflow flux at streamside. Soil moisture content at hillslope decreased continuously after rain, and showed a negative correlation to sapflow flux like a soil water tension at hillslope. As considered results above, it was confirmed that the response of soil moisture tension to sapflow flux at hillslope and streamside were different.

• The Journal of Korean Academy of Prosthodontics
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• v.47 no.2
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• pp.108-118
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• 2009

Statement of problem: Volume stability, microstructure reproducibility and fluidity along with compatibility with dental stone must be in consideration in order to use tissue conditioner as a material for functional impression. There are few studies concerning the influence of time factor in oral condition on surface roughness of the stone and optimal retention period in the oral cavity considering such changes in surface roughness. Purpose: The purpose of this study was to find out the influence of various kinds of tissue conditioner, its powder/liquid ratio and immersion time on surface roughness of the stone. Material and methods: Materials used in this study were the three kinds of tissue conditioners(Coe-Comfort, Visco-Gel, Soft-Liner) and were grouped into three: group R-mixed with standard powder/liquid ratio that was recommended by the manufacturers, group M-mixed with 20% more powder, group L-mixed with 20% less powder. Specimens were made with the size of 20 mm diameter and 2 mm width. Each tissue conditioner specimens were subdivided into 5 groups according to the immersion time(0 hour, 1 day, 3 days, 5 days, 7 days), completely immersed into artificial saliva and were stored under $37^{\circ}C$. Specimens of which the given immersion time elapsed were taken out and were poured with improved stone, making the stone specimens. Surface roughness of the stone specimens was measured by a profilometer. Results: Within the limitation of this study, the following results were drawn. 1. Major influencing factor on surface roughness of the stone model made from tissue conditioner was the retention period(contribution ratio($\rho$)=62.86%, P<.05) of the tissue conditioner in oral cavity to make functional impression. 2. In case of Coe-Comfort, higher mean surface roughness value of the stone model with statistical significance was observed compared to that of Soft-Liner and Visco-Gel as immersion time changes(P<.05). 3. In case of group L(less), higher mean surface roughness value of the stone model with statistical significance was observed compared to that of R(recommended) and M(more) group as immersion time changes(P<.05). Conclusion: We may conclude that as the retention period of time in oral cavity influences surface roughness of the stone model the most and as the kind of tissue conditioner and its P/L ratio may influence also, clinician should well understand the optimal retention period in oral cavity and choose the right tissue conditioner for the functional impression, thus making the functional impression with tissue conditioner usefully.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.