• Advances in aircraft and spacecraft science
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• v.1 no.3
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• pp.311-330
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• 2014

Environmental impact of aircraft emissions can be addressed in two ways. Air quality impact occurs during landings and takeoffs while in-flight impact during climbs and cruises influences climate change, ozone and UV-radiation. The aim of this paper is to investigate airports related local emissions and fuel consumption (FC). It gives flight path optimization model linked to a dispersion model as well as numerical methods. Operational factors are considered and the cost function integrates objectives taking into account FC and induced pollutant concentrations. We have compared pollutants emitted and their reduction during LTO cycles, optimized flight path and with analysis by Dopelheuer. Pollutants appearing from incomplete and complete combustion processes have been discussed. Because of calculation difficulties, no assessment has been made for the soot, $H_2O$ and $PM_{2.5}$. In addition, because of the low reliability of models quantifying pollutant emissions of the APU, an empirical evaluation has been done. This is based on Benson's fuel flow method. A new model, giving FC and predicting the in-flight emissions, has been developed. It fits with the Boeing FC model. We confirm that FC can be reduced by 3% for takeoffs and 27% for landings. This contributes to analyze the intelligent fuel gauge computing the in-flight fuel flow. Further research is needed to define the role of $NO_x$ which is emitted during the combustion process derived from the ambient air, not the fuel. Models are needed for analyzing the effects of fleet composition and engine combinations on emission factors and fuel flow assessment.

• Asia Pacific Journal of Business Review
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• v.1 no.2
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• pp.37-62
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• 2017

To analyze the effects of R&D expenditure on the firm value of Korean firms, we classified portfolios based on R&D activity levels. After that, we conducted a time-series analysis to assess excess returns from the portfolios. To carry out such an analysis, an empirical analysis of excess returns in the capital market was performed by using the monthly earning rate of stocks from 2000 to 2013. The purpose of this research is to provide basic data on investment to stakeholders in the capital market by analyzing the effects of R&D on the firm value and to overcome scholarly limitations by offering a new model of analysis. The criteria for classifying the portfolios were based on R&D expenditure levels. The analysis models follow the Fama-French Three-Factor Model and the Carhart Four-Factor Model. The analyses results are as follows. Extrapolating monthly profit rates based on R&D expenditure levels, portfolios with low R&D expenditures showed higher earning rates than those with high R&D expenditures. This suggests that high R&D expenditures did not translate into high earning rates. The investor depreciates the R&D expenditures related profitability and the possibility of success in the market, leading to falls in stock prices and a failure to give a positive effect on the firm value. Our research differs from the previous investigations as we carried out an empirical analysis based on the actual investors' attitudes about R&D expenditures and how these can generate excess earnings. Our research results show that the data related to R&D expenditure are not reflected fully in the market.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• Management & Information Systems Review
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• v.29 no.2
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• pp.1-25
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• 2010

This study attempted to empirically test the determinants of stock returns in Korean stock market applying multi-factor model proposed by Haugen and Baker(1996). Regression models were developed using 16 variables related to liquidity, risk, historical price, price level, and profitability as independent variables and 690 stock monthly returns as dependent variable. For the statistical analysis, the data were collected from the Kis Value database and the tests of forecasting power in this study minimized various possible bias discussed in the literature as possible. The statistical results indicated that: 1) Liquidity, one-month excess return, three-month excess return, PER, ROE, and volatility of total return affect stock returns simultaneously. 2) Liquidity, one-month excess return, three-month excess return, six-month excess return, PSR, PBR, ROE, and EPS have an antecedent influence on stock returns. Meanwhile, realized returns of decile portfolios increase in proportion to predicted returns. This results supported previous study by Haugen and Baker(1996) and indicated that firm-characteristic model can better predict stock returns than CAPM. 3) The firm-characteristic model has better predictive power than Fama-French three-factor model, which indicates that a portfolio constructed based on this model can achieve excess return. This study found that expected return factor models are accurate, which is consistent with other countries' results. There exists a surprising degree of commonality in the factors that are most important in determining the expected returns among different stocks.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.489-502
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• 2022

In this study, a Gaussian process regression (GPR) model as well as six GPR-based metaheuristic optimization models, including GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, and GPR-SSO, were developed to predict fly-rock distance in the blasting operation of open pit mines. These models included GPR-SCA, GPR-SSO, GPR-MVO, and GPR. In the models that were obtained from the Soungun copper mine in Iran, a total of 300 datasets were used. These datasets included six input parameters and one output parameter (fly-rock). In order to conduct the assessment of the prediction outcomes, many statistical evaluation indices were used. In the end, it was determined that the performance prediction of the ML models to predict the fly-rock from high to low is GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, GPR-SSO, and GPR with ranking scores of 66, 60, 54, 46, 43, 38, and 30 (for 5-fold method), respectively. These scores correspond in conclusion, the GPR-PSO model generated the most accurate findings, hence it was suggested that this model be used to forecast the fly-rock. In addition, the mutual information test, also known as MIT, was used in order to investigate the influence that each input parameter had on the fly-rock. In the end, it was determined that the stemming (T) parameter was the most effective of all the parameters on the fly-rock.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.11
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• pp.1447-1456
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• 2000

Simulated experiments of high pressure melt ejection(HPME) are performed to measure the released fraction of corium simulant from the French type PWR cavity. The experiments are carried out on a 1/20th linear scaled model of the Ulchin 1&2 cavity. Water or woods metal and nitrogen is used as simulant of molten corium and steam, respectively. Experimental parameters are water mass, annulus area and breach size. It is shown that only breach size effects is very important while the mass and the annulus area do not affect the released fraction. It is found that the liquid film transport is much more dominant mechanism than the entrainment droplet transport, especially in linear scale down simulated HPME experiment.

• Journal of Applied Tourism Food and Beverage Management and Research
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• v.16 no.2
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• pp.17-31
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• 2005

Recently, hotel industry has realized the importance of food and beverage sales for the profit maximization, and the focuses on restaurant management has been growing. Accordingly, menu management in the F/B department is one of the most key factors determining the success or failure of business. Therefore, in this study, entree menu items of french restaurant in the deluxe hotel was analysed with presently theorized model of menu analysis, classified into four menu items. Also it was analyzed how those classified menu items influence on sales, number of sold, food cost percentage, contribution margin And, proper ways was presented to make restaurant managers and menu planner in order to increase food operation sales through proper modifications and methods on various menu analysis matrix. In Pavesic's menu analysis method, all of menu items have impact on the sales, number of sold, contribution margin and Primes, Sleepers do so on the food cost. The finding of this study was that Pavesic's menu analysis is superior to menu analysis in terms of the sales, number of sold, food cost percentage, contribution margin. Therefore, Pavesic's menu analysis is useful and efficient method in order to conduct menu engineering.

• Proceedings of the Korea Hospitality Industry Research Society Conference
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• 2005.11a
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• pp.80-91
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• 2005

Recently, hotel industry has realized the importance of food and beverage sales for the profit maximization, and the focuses on restaurant management has been growing. Accordingly, menu management in the F/B department is one of the most key factors determining the success or failure of business. Therefore, in this study, entree menu items of french restaurant in the deluxe hotel was analysed with presently theorized model of menu analysis, classified into four menu items. Also it was analyzed how those classified menu items influence on sales, number of sold, food cost percentage, contribution margin And, proper ways was presented to make restaurant managers and menu planner in order to increase food operation sales through proper modifications and methods on various menu analysis matrix. In Pavesic's menu analysis method, all of menu items have impact on the sales, number of sold, contribution margin and Primes, Sleepers do so on the food cost. The finding of this study was that Pavesic's menu analysis is superior to menu analysis in terms of the sales, number of sold, food cost percentage, contribution margin. Therefore, Pavesic's menu analysis is useful and efficient method in order to conduct menu engineering.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.2
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• pp.43-52
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• 2020

The paper investigates the mechanism through which corporate credit ratings affect dividend payments by decomposing the mean difference of dividends into a part that is explained by the determinants of dividends and a residual part that is contributed by the pure credit group effect, in the framework of the traditional dividend model of Fama and French (2001). Historically, better credit rated firms have shown consistently higher propensity to pay dividends especially during the economic crisis period. According to the counter-factual decomposition technique of Jann (2008), better rated firms are more responsive to the firm characteristics that have positive impact on dividends and poor rated firms are more responsive to the negative dividend predictors. As a result, good (bad) credit ratings make corporate managers become more bold (timid) in their dividend payments and they tend to pay more (less) dividends than what their firm characteristics prescribe. The degree of information asymmetry increases for the poor group firms during crisis periods and they attempt to reserve more cash in preparation for future investments. The decomposition results suggest that the credit group effect can potentially exceed the effect of firm characteristics because firms of different credit ratings can respond to the very same firm characteristics in a different manner.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.2
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• pp.33-42
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• 2020

The paper investigates the mechanism through which corporate credit ratings affect dividend payments by decomposing the mean difference of dividends into a part that is explained by the determinants of dividends and a residual part that is contributed by the pure credit group effect, in the framework of the traditional dividend model of Fama and French (2001). Historically, better credit rated firms have shown consistently higher propensity to pay dividends especially during the economic crisis period. According to the counter-factual decomposition technique of Jann (2008), better rated firms are more responsive to the firm characteristics that have positive impact on dividends and poor rated firms are more responsive to the negative dividend predictors. As a result, good (bad) credit ratings make corporate managers become more bold (timid) in their dividend payments and they tend to pay more (less) dividends than what their firm characteristics prescribe. The degree of information asymmetry increases for the poor group firms during crisis periods and they attempt to reserve more cash in preparation for future investments. The decomposition results suggest that the credit group effect can potentially exceed the effect of firm characteristics because firms of different credit ratings can respond to the very same firm characteristics in a different manner.