• Journal of Applied Biological Chemistry
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• v.62 no.2
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• pp.195-202
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• 2019

Rhus javanica L. is Anacardiaceae plant distributed in East Asia. We evaluated the antioxidant activity and antiinflammatory effect of leaf, branch, root of ethyl acetate fraction from R. javanica. To confirm effective each extraction, The antioxidant activity was evaluated using 1,1-Diphenyl-2-picryl-hydrazyl and 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) scavenging activity assays, and the anti-inflammatory activity was evaluated based on inhibitory activities on the protein and mRNA expression of iNOS and COX-2 in LPS-induced RAW264.7 cells. The phenolic compounds content of each extract was analyzed with Folin reagents and HPLC/PDA method. The gallic acids were identified and quantified. The roots of R. javanica showed strong antioxidant activity. Its total phenolic compounds content were higher than the orders. In addition, anti-inflammatory activity inhibited the protein and mRNA expression of nitric oxide production factor, following the same pattern as contents of phenolic compounds included gallic acid and its antioxidant activity. In conclusion, R. javanica showed effective antioxidant and anti-inflammatory activity. Especially, the roots were evaluated to be highly valuable as a natural resource for reducing inflammation.

• Fire Science and Engineering
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• v.33 no.2
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• pp.107-113
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• 2019

Recently, due to global warming, the trend shows an increase in number of lightning strikes which increase risk regarding industry infrastructures. Especially in case where the lightning strikes infrastructures including refinery, petorchemical plant facilities or storage tanks, it can cause power failures, electrical machine malfunction and damage which can lead to fire explosion and multiple calamities. Therefore, detailed case studies must be conducted through a systematic research regarding lightning strike accidents in order to understand its mechanism and devise preventive measures. This paper aims to study cases of explosion regarding waste water storage tanks in refineries and petrochemical plants in order to analyze its root cause and provide preventive measures for avoiding lightning related incidents.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.219-228
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• 2019

The forest of the Huaphanh Province (HP) has continued to decrease at 0.6% (10,560 ha) per year from 1992 to 2010. In the past few decades, the government of Laos and the Huaphanh Provincial Authority have been trying to address the root causes of deforestation. This study attempts to examine the factors effecting the decrease of the forest cover in the HP by analyzing the influence of the local socio-economic development and implementation of forest management policies on changes in the forest cover. The social data of the province focused on population growth and distribution between urban and rural areas including the number of poor people and the economic growth of three sectors, namely agriculture and forestry, industry, and service, while the implementation of the state forest management policy focused on the state forest management plan, tree plantation, forest land use planning and allocation to households, and shifting cultivation including annual upland rice and maize cultivation. In addition, government reports on socio-economic and rural development including poverty eradication of other provinces, where an increase in the forest cover was observed, were also collected and analyzed using qualitative and comparative analysis. The results from this study indicate that the decrease in forest cover in the Huaphanh Province appears to depend on a very slow economic growth and reduction in rural poverty of the province. The increase in the rural population in the province led to an increase in farm households and are as for shifting cultivation. As a result, forests were cleared leading to a decrease in the forest cover.

• Asia-Pacific Journal of Business
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• v.11 no.2
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• pp.159-172
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• 2020

Purpose - The purpose of this study is to investigate economic variables which have impact on the prices and returns of preferred stocks and to provide investors, underwriters, and policy makers with information regarding correlations and causal relations between them. Design/methodology/approach - This study collected 98 monthly data from Korea Exchange and Bank of Korea. The Granger causal relation analysis, unit-root test and the multiple regression analysis were hired in order to analyze the data. Findings - First, our study derives the economic variables affecting the prices and returns of preferred stocks and their implications, while previous studies focused mainly on the differential characteristics and related economic factors between common and preferred stocks. Empirical results show that the significant variables influencing the prices and returns of preffered stocks are consumer sentiment index, consumer price index, industrial production index, KOSPI volatility index, and exchange rate between Korean won and US dollar. Second, consumer sentiment index, consumer price index, and industrial production index have significant casual relations with the returns of preferred stocks, providing market participants with important information regarding investment in preferred stocks. Research implications or Originality - This study is different from previous studies in that preferred stocks themselves are investigated rather than the gap between common stocks and preferred stocks. In addition, we derive the major macro variables affecting the prices and returns of preferred stocks and find some useful causal relations between the macro variables and returns of preferred stocks. These findings give important implications to market participants, including stock investors, underwriters, and policy makers.

• Safety and Health at Work
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• v.13 no.2
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• pp.170-179
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• 2022

Background: The vulnerability of international migrant workers is on the rise, affecting the frequency of occupational accidents at workplaces worldwide. If migrant workers are managed in the same way as native workers, the consequences on safety assurance and risk management will be significant. This study aimed to develop the vulnerability factor model for migrant workers in seafood processing industries because of significant risk-laden labor of Thailand, which could be a solution to control the risk effectively. Methods: A total of 569 migrant workers were surveyed (432 Burmese and 137 Cambodian), beginning with 40 initial vulnerability factors identified in the questionnaire established from experts. The data were analyzed through descriptive analysis; exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to ascertain the model. Results: The result of content validity >0.67 and the Cronbach's alpha of 0.957 specified the high reliability of 40 factors. The EFA indicated a total variance of 65.49%. The final CFA validated the model and had an empirical fitting; chi-square = 85.34, Adjust Goodness-of-Fit Index = 0.96, and root mean square error of approximation = 0.016. The structure concluded with three dimensions and 18 factors. Dimension 1 of the structure, "multicultural safety operation," contained 12 factors; Dimension 2, "wellbeing," contained four factors; and Dimension 3, "communication technology," contained two factors. Conclusion: The vulnerability factor structure developed in this study included three dimensions and 18 factors that were significantly empirical. The knowledge enhanced safety management in the context of vulnerability factor structure for migrant workers at the workplace.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.279-290
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• 2022

The popularization of mobile Internet applications has accelerated the development of smart tourism industry. Based on TAM and VAM theories, this paper studies the influencing factors of tourism App users' willingness to continue using through complex network and data analysis methods. Through the research, it is found that the improvement of service level and design concept of smart tourism application can accelerate the aggregation of complex networks and improve user engagement. At the same time, reasonable price service experience value, convenience service experience value, interactive service experience value, emotional design perception, ease of use design perception, entertainment design perception and other factors can have a direct impact on users' intention to continue to use, and there is a significant correlation. The smart tourism App's convenience and price advantage are the root of its competitiveness. The design concept can affect users' emotional experience and perceptual experience, and help smart tourism App improve users' satisfaction.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.515-535
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• 2022

The strength models for fiber-reinforced polymer (FRP)-confined normal strength concrete (NC) cylinders available in the literature have been suggested based on small databases using limited variables of such structural members portraying less accuracy. The artificial neural network (ANN) is an advanced technique for precisely predicting the response of composite structures by considering a large number of parameters. The main objective of the present investigation is to develop an ANN model for the axial strength of FRP-confined NC cylinders using various parameters to give the highest accuracy of the predictions. To secure this aim, a large experimental database of 313 FRP-confined NC cylinders has been constructed from previous research investigations. An evaluation of 33 different empirical strength models has been performed using various statistical parameters (root mean squared error RMSE, mean absolute error MAE, and coefficient of determination R²) over the developed database. Then, a new ANN model using the Group Method of Data Handling (GMDH) has been proposed based on the experimental database that portrayed the highest performance as compared with the previous models with R²=0.92, RMSE=0.27, and MAE=0.33. Therefore, the suggested ANN model can accurately capture the axial strength of FRP-confined NC cylinders that can be used for the further analysis and design of such members in the construction industry.

• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.174-179
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• 2023

Bulk trailers, used for the transportation of powdered materials, such as cement and fly ash, are crucial in the construction industry. The speedy exhaustion of powdered materials stored in the tank of bulk trailers is relevant to improving transportation efficiency and reducing transportation costs. The exhaust time can be reduced by developing an automatic control system to replace the manual exhaust operation. The instantaneous or accumulated exhausts of powdered materials must be measured for automatic control of the bulk trailer exhaust system. Accordingly, we previously proposed a recurrent neural network (RNN) model that estimated the instantaneous exhaust based on low-cost pressure sensor signals without an expensive flowmeter for powders. Although our previous study utilized only an RNN model, models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are also widely utilized for time-series estimation. This study compares the performance of three neural network models (MLP, CNN, and RNN) in estimating instantaneous and accumulated exhausts. In terms of the instantaneous exhaust estimation, the difference in the performance of neural network models was insignificant (that is, 8.64, 8.62, and 8.56% for the MLP, CNN, and RNN, respectively, in terms of the normalized root mean squared error). However, in the case of the accumulated exhaust, the performance was excellent in the order of CNN (1.67%), MLP (2.03%), and RNN (2.20%).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• Food Science of Animal Resources
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• v.43 no.6
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• pp.1150-1169
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• 2023

Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R² values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.