• Journal of Chest Surgery
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• v.32 no.7
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• pp.603-612
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• 1999

Background : It has been documented that brief repetitive periods of ischemia and reperfusion (ischemic preconditioning, IP) enhances the recovery of post-ischemic contractile function and reduces infarct size after a longer period of ischemia. Many mechanisms have been proposed to explain this process. Recent studies have suggested that transient increase in the intracellular calcium may have triggered the activation of protein kinase C(PKC); however, there are still many controversies. Accordingly, the author performed the present study to test the hypothesis that preconditioning with high concentration of calcium before sustained subsequent ischemia(calcium preconditioning) mimics IP by PKC activation. Material and Method : The isolated hearts from the New Zealand White rabbits(1.5∼2.0 kg body weight) Method: The isolated hearts from the New Zealand White rabbits(1.5∼2.0 kg body weight) were perfused with Tyrode solution by Langendorff technique. After stabilization of baseline hemodynamics, the hearts were subjected to 45-minute global ischemia followed by a 120-minute reperfusion with IP(IP group, n=13) or without IP(ischemic control, n=10). IP was induced by single episode of 5-minute global ischemia and 10-minute reperfusion. In the Ca2+ preconditioned group, perfusate containing 10(n=10) or 20 mM(n=11) CaCl2 was perfused for 10 minutes after 5-minute ischemia followed by a 45-minute global ischemia and a 120-minute reperfusion. Baseline PKC was measured after 50-minute perfusion without any treatment(n=5). Left ventricular function including developed pressure(LVDP), dP/dt, heart rate, left ventricular end-diastolic pressure(LVEDP) and coronary flow(CF) was measured. Myo car ial cytosolic and membrane PKC activities were measured by 32P-${\gamma}$-ATP incorporation into PKC-specific pepetide. The infarct size was determined using the TTC (tetrazolium salt) staining and planimetry. Data were analyzed using one-way analysis of variance(ANOVA) variance(ANOVA) and Tukey's post-hoc test. Result: IP increased the functional recovery including LVDP, dP/dt and CF(p<0.05) and lowered the ascending range of LVEDP(p<0.05); it also reduced the infarct size from 38% to 20%(p<0.05). In both of the Ca2+ preconditioned group, functional recovery was not significantly different in comparison with the ischemic control, however, the infarct size was reduced to 19∼23%(p<0.05). In comparison with the baseline(7.31 0.31 nmol/g tissue), the activities of the cytosolic PKC tended to decrease in both the IP and Ca2+ preconditioned groups, particularly in the 10 mM Ca2+ preconditioned group(4.19 0.39 nmol/g tissue, p<0.01); the activity of membrane PKC was significantly increased in both IP and 10 mM Ca2+ preconditioned group (p<0.05; 1.84 0.21, 4.00 0.14, and 4.02 0.70 nmol/g tissue in the baseline, IP, and 10 mM Ca2+ preconditioned group, respectively). However, the activity of both PKC fractions were not significantly different between the baseline and the ischemic control. Conclusion: These results indicate that in isolated Langendorff-perfused rabbit heart model, calcium preconditioning with high concentration of calcium does not improve post-ischemic functional recovery. However, it does have an effect of limiting(reducing) the infart size by ischemic preconditioning, and this cardioprotective effect, at least in part, may have resulted from the activation of PKC by calcium which acts as a messenger(or trigger) to activate membrane PKC.

• Journal of Chest Surgery
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• v.33 no.7
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• pp.531-540
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• 2000

Baclgrpimd; Recent studies have suggested that the cardioprotective effect of ischemic preconditioning(IP) is closely related to glycogen depletion and attenuation of intracellular acidosis. In the present study, the authors tested this hypothesis by perfusion isolated rabbit hearts with glucose(G) is closely related to glycogen depletion and attenuation of intracellular acidosis. In the present study, the authors tested this hypothesis by perfusion isolated rabbit hearts with glucose(G)-free perfusate. Material and Method; Hearts isolated from New Zealand white rabbits(1.5~2.0 kg body weight) were perfused with Tyrode solution by Langendorff technique. After stabilization of baseline hemodynamics, the hearts were subjected to 45 min global ischemia followed by 120 min reperfusion with IP(IP group, n=13) or without IP(ischemic control group, n=10). IP was induced by single episode of 5 min global ischemia and 10 min reperfusion. In the G-free preconditioned group(n=12), G depletion was induced by perfusionwith G-free Tyrode solution for 5 min and then perfused with G-containing Tyrode solution for 10 min; and 45 min ischemia and 120 min reperfusion. Left ventricular functionincluding developed pressure(LVDP), dP/dt, heart rate, left ventricular end-distolic pressure(LVEDP) and coronary flow (CF) were measured. Myocardial cytosolic and membrane PKC activities were measured by 32P-${\gamma}$-ATP incorporation into PKC-specific peptide and PKC isozymes were analyzed by Western blot with monoclonal antibodies. Infarct size was determined by staining with TTC(tetrazolium salt) and planimetry. Data were analyzed by one-way analysis of variance (ANOVA) and Turkey's post-hoc test. Result ; In comparison with the ischemic control group, IP significantly enhanced functional recovery of the left ventricle; in contrast, functional significantly enhanced functional recovery of the left ventricle; in contrast, functional recovery were not significantly different between the G-free preconditioned and the ischemic control groups. However, the infarct size was significantly reduced by IP or G-free preconditioning(39$\pm$2.7% in the ischemic control, 19$\pm$1.2% in the IP, and 15$\pm$3.9% in the G-free preconditioned, p<0.05). Membrane PKC activities were increased significantly after IP (119%), IP and 45 min ischemia(145%), G-free [recpmdotopmomg (150%), and G-free preconditioning and 45 min ischemia(127%); expression of membrane PKC isozymes, $\alpha$ and $\varepsilon$, tended to be increased after IP or G-free preconditioning. Conclusion; These results suggest that in isolated Langendorff-perfused rabbit heart model, G-free preconditioning (induced by single episode of 5 min G depletion and 10 min repletion) colud not improve post-ischemic contractile dysfunction(after 45-minute global ischemia); however, it has an infarct size-limiting effect.

• 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.

• Journal of Bio-Environment Control
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• v.5 no.2
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• pp.215-235
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• 1996

The thermal analysis by mathematical model simulation makes it possible to reasonably predict heating and/or cooling requirements of certain greenhouses located under various geographical and climatic environment. It is another advantages of model simulation technique to be able to make it possible to select appropriate heating system, to set up energy utilization strategy, to schedule seasonal crop pattern, as well as to determine new greenhouse ranges. In this study, the control pattern for greenhouse microclimate is categorized as cooling and heating. Dynamic model was adopted to simulate heating requirements and/or energy conservation effectiveness such as energy saving by night-time thermal curtain, estimation of Heating Degree-Hours(HDH), long time prediction of greenhouse thermal behavior, etc. On the other hand, the cooling effects of ventilation, shading, and pad ||||&|||| fan system were partly analyzed by static model. By the experimental work with small size model greenhouse of 1.2m$\times$2.4m, it was found that cooling the greenhouse by spraying cold water directly on greenhouse cover surface or by recirculating cold water through heat exchangers would be effective in greenhouse summer cooling. The mathematical model developed for greenhouse model simulation is highly applicable because it can reflects various climatic factors like temperature, humidity, beam and diffuse solar radiation, wind velocity, etc. This model was closely verified by various weather data obtained through long period greenhouse experiment. Most of the materials relating with greenhouse heating or cooling components were obtained from model greenhouse simulated mathematically by using typical year(1987) data of Jinju Gyeongnam. But some of the materials relating with greenhouse cooling was obtained by performing model experiments which include analyzing cooling effect of water sprayed directly on greenhouse roof surface. The results are summarized as follows : 1. The heating requirements of model greenhouse were highly related with the minimum temperature set for given greenhouse. The setting temperature at night-time is much more influential on heating energy requirement than that at day-time. Therefore It is highly recommended that night- time setting temperature should be carefully determined and controlled. 2. The HDH data obtained by conventional method were estimated on the basis of considerably long term average weather temperature together with the standard base temperature(usually 18.3$^{\circ}C$). This kind of data can merely be used as a relative comparison criteria about heating load, but is not applicable in the calculation of greenhouse heating requirements because of the limited consideration of climatic factors and inappropriate base temperature. By comparing the HDM data with the results of simulation, it is found that the heating system design by HDH data will probably overshoot the actual heating requirement. 3. The energy saving effect of night-time thermal curtain as well as estimated heating requirement is found to be sensitively related with weather condition: Thermal curtain adopted for simulation showed high effectiveness in energy saving which amounts to more than 50% of annual heating requirement. 4. The ventilation performances doting warm seasons are mainly influenced by air exchange rate even though there are some variations depending on greenhouse structural difference, weather and cropping conditions. For air exchanges above 1 volume per minute, the reduction rate of temperature rise on both types of considered greenhouse becomes modest with the additional increase of ventilation capacity. Therefore the desirable ventilation capacity is assumed to be 1 air change per minute, which is the recommended ventilation rate in common greenhouse. 5. In glass covered greenhouse with full production, under clear weather of 50% RH, and continuous 1 air change per minute, the temperature drop in 50% shaded greenhouse and pad ＆ fan systemed greenhouse is 2.6$^{\circ}C$ and.6.1$^{\circ}C$ respectively. The temperature in control greenhouse under continuous air change at this time was 36.6$^{\circ}C$ which was 5.3$^{\circ}C$ above ambient temperature. As a result the greenhouse temperature can be maintained 3$^{\circ}C$ below ambient temperature. But when RH is 80%, it was impossible to drop greenhouse temperature below ambient temperature because possible temperature reduction by pad ||||&|||| fan system at this time is not more than 2.4$^{\circ}C$. 6. During 3 months of hot summer season if the greenhouse is assumed to be cooled only when greenhouse temperature rise above 27$^{\circ}C$, the relationship between RH of ambient air and greenhouse temperature drop($\Delta$T) was formulated as follows : $\Delta$T= -0.077RH＋7.7 7. Time dependent cooling effects performed by operation of each or combination of ventilation, 50% shading, pad ＆ fan of 80% efficiency, were continuously predicted for one typical summer day long. When the greenhouse was cooled only by 1 air change per minute, greenhouse air temperature was 5$^{\circ}C$ above outdoor temperature. Either method alone can not drop greenhouse air temperature below outdoor temperature even under the fully cropped situations. But when both systems were operated together, greenhouse air temperature can be controlled to about 2.0-2.3$^{\circ}C$ below ambient temperature. 8. When the cool water of 6.5-8.5$^{\circ}C$ was sprayed on greenhouse roof surface with the water flow rate of 1.3 liter/min per unit greenhouse floor area, greenhouse air temperature could be dropped down to 16.5-18.$0^{\circ}C$, whlch is about 1$0^{\circ}C$ below the ambient temperature of 26.5-28.$0^{\circ}C$ at that time. The most important thing in cooling greenhouse air effectively with water spray may be obtaining plenty of cool water source like ground water itself or cold water produced by heat-pump. Future work is focused on not only analyzing the feasibility of heat pump operation but also finding the relationships between greenhouse air temperature(T$_{g}$ ), spraying water temperature(T$_{w}$ ), water flow rate(Q), and ambient temperature(T$_{o}$).