• Journal of People, Plants, and Environment
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• v.24 no.2
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• pp.219-227
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• 2021

Background and objective: Urban street trees play an important role in carbon reduction in cities where greenspace is scarce. There are ongoing studies on carbon reduction by street trees. However, information on the carbon reduction capacity of street trees based on field surveys is still limited. This study aimed to quantify carbon uptake and storage by urban street trees and suggest a method to improve planting of trees in order to increase their carbon reduction capacity. Methods: The cities selected were Sejong, Chungju, and Jeonju among cities without research on carbon reduction, considering the regional distribution in Korea. In the cities, 155 sample sites were selected using systematic sampling to conduct a field survey on street environments and planting structures. The surveyed data included tree species, diameter at breast height (DBH), diameter at root collar (DRC), height, crown width, and vertical structures. The carbon uptake and storage per tree were calculated using the quantification models developed for the urban trees of each species. Results: The average carbon uptake and storage of street trees were approximately 7.2 ± 0.6 kg/tree/yr and 87.1 ± 10.2 kg/tree, respectively. The key factors determining carbon uptake and storage were tree size, vertical structure, the composition of tree species, and growth conditions. The annual total carbon uptake and storage were approximately 1,135.8 tons and 22,737.8 tons, respectively. The total carbon uptake was about the same amount as carbon emitted by 2,272 vehicles a year. Conclusion: This study has significance in providing the basic unit to quantify carbon uptake and storage of street trees based on field surveys. To improve the carbon reduction capacity of street trees, it is necessary to consider planning strategies such as securing and extending available grounds and spaces for high-density street trees with a multi-layered structure.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.243-251
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• 2022

In this paper, we propose a phenomenon that analyze the impact of market sentiment on China's real estate market through the perspective of behavioral economics. Previously, real estate market analyzation basically focus on some fundamental principles which include market price, monetary policies and income, etc. However, little research has explored market sentiment and its influence. By using principal components analysis (PCA), this study first creates buyer's sentiment and seller's sentiment to measure the heat of China's real estate market. Different from using traditional estimation method, the vector autoregressive model (VAR) is used to analyze how both sentiments affect real estate return. The overall results show that from unit root test and impulse response analyzation, the impact of seller's sentiment is positive to real estate market while buyer's sentiment is negative. At the same time, the higher seller's sentiment will have different influence on the housing market compared with the higher buyer's sentiment.

• The Journal of Fisheries Business Administration
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• v.52 no.4
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• pp.13-28
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• 2021

The purpose of this study is to compare short-term price predictive power among ARMA ARMAX and VAR forecasting models based on the MDM test using monthly consumer price data of frozen mackerel. This study also aims to help policymakers and economic actors make reasonable choices in the market on monthly consumer price of frozen mackerel. To analyze this study, the frozen wholesale prices and new consumer prices were used as variables while the price time series data were used from December 2013 to July 2021. Through the unit root test, it was confirmed that the time series variables employed in the models were stable while the level variables were used for analysis. As a result of conducting information standards and Granger causality tests, it was found that the wholesale prices and fresh consumer prices from the previous month have affected the frozen consumer prices. Then, the model with the highest predictive power was selected by RMSE, RMSPE, MAE, MAPE, and Theil's inequality coefficient criteria where the predictive power was compared by the MDM test in order to examine which model is superior. As a result of the analysis, ARMAX(1,1) with the frozen wholesale, ARMAX(1,1) with the fresh consumer model and VAR model were selected. Through the five criteria and MDM tests, the VAR model was selected as the superior model in predicting the monthly consumer price of frozen mackerel.

• Proceedings of the SCSK Conference
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• 2003.09a
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• pp.480-490
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• 2003

An innovative biopolymer known as the Rhizobian gum has been developed in France, which shows some dramatic refreshing effect on the skin. The origin of this innovative project takes its source in the natural environment, and in particular the natural environment of the roots of sunflowers and wheat, where a symbiotic bacterium has been discovered. It is a Rhizobium bacterium, which is hosted by the roots, and which is able to synthesize a specific polymer showing a dramatic water binding capacity. This polymer is in particular synthesized in period of drought, and its biological role is to concentrate the small amount water present in the soil in order to take it available for the root, which becomes then able to absorb it. This vital mechanism allows the plant to survive despite a severe climatic environment. This basic research has been conducted in collaboration whit the French National centre of scientific Research (CNRS), and has lead to the isolation of the Rhizobium bacteria. Rhizobian gum is a branched biopolymer consisting in the repetition of a polysaccharide unit of 3 molecules of glucose, 3 molecules of galactose and 1 molecule of glucuronic acid, whit one pyruvate group an average 1.6 acetyl groups. The fresh effect of Rhizobian gum is a strong sensorial impact that 100 ％ of the consumers are able to perceive, and which is judged very pleasant by most of them. In addition to this, a large majority of consumers are perceived, and which is judge very pleasant by most of them. In addition to this, a large majority of consumers also feel a very pleasant relaxing sensation. Smoothness and softness are also felt by most consumers and qualified positively by most of them. These qualities guarantee a strong impact on today's consumers.

• Proceedings of the SCSK Conference
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• 2003.09a
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• pp.50-60
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• 2003

An innovative biopolymer known as the Rhizobian gum has been developed in France, which shows some dramatic refreshing effect on the skin. The origin of this innovative project takes its source in the natural environment, and in particular the natural environment of the roots of sunflowers and wheat, where a symbiotic bacterium has been discovered. It is a Rhizobium bacterium, which is hosted by the roots, and which is able to synthesize a specific polymer showing a dramatic water binding capacity. This polymer is in particular synthesized in period of drought, and its biological role is to concentrate the small amount water present in the soil in order to take it available for the root, which becomes then able to absorb it. This vital mechanism allows the plant to survive despite a severe climatic environment. This basic research has been conducted in collaboration whit the French National centre of scientific Research (CNRS), and has lead to the isolation of the Rhizobium bacteria. Rhizobian gum is a branched biopolymer consisting in the repetition of a polysaccharide unit of 3 molecules of glucose, 3 molecules of galactose and 1 molecule of glucuronic acid, whit one pyruvate group an average 1.6 acetyl groups. The fresh effect of Rhizobian gum is a strong sensorial impact that 100 ％ of the consumers are able to perceive, and which is judged very pleasant by most of them. In addition to this, a large majority of consumers are perceived, and which is judge very pleasant by most of them. In addition to this, a large majority of consumers also feel a very pleasant relaxing sensation. Smoothness and softness are also felt by most consumers and qualified positively by most of them. These qualities guarantee a strong impact on today's consumers.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.697-726
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• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.617-632
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• 2022

In recent years, environmental pollution and determining the main factors causing this pollution have become an important issue. This study investigates the relationship between the agricultural sector and environmental pollution in Azerbaijan for 1992-2018. The dependent variable in the study is the agricultural greenhouse gas emissions (CO₂ equivalent). Eight variables were selected as explanatory variables: four agricultural inputs and four agricultural macro indicators. Unit root tests, ARDL boundary test, FMOLS, DOLS and CCR long-term estimators, Granger causality analysis, and variance decomposition analyses were used to investigate the effect of these variables on agricultural emissions. The results show that chemical fertilizer consumption, livestock number, and pesticide use positively and statistically significantly affect agricultural emissions from agricultural input variables. In contrast, agricultural energy consumption has a negative and significant effect. From agricultural macro indicator variables, it was found that the crop and animal production index had a positive and significant effect on agricultural emissions. According to the Granger causality test results, it was concluded that there are a causality relationship from chemical fertilizer consumption, livestock number, crop and livestock production index variables towards agricultural emissions. Considering all the results obtained, it is seen that the variables that have the most effect on the increase in agricultural emissions in Azerbaijan are the number of livestock, the consumption of chemical fertilizers, and the use of pesticides, respectively. The results from the research will contribute to the information on agricultural greenhouse gas emissions and will play an enlightening role for policymakers and the general public.

• Asia-Pacific Journal of Business
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• v.14 no.1
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• pp.149-169
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• 2023

Purpose - The main purpose of this study is to develop the stress test model for Korean banks by exploring the optimal Monte Carlo simulation and BIS forecasting model. Design/methodology/approach - This study selects 15 Korean banks as sample financial firms and collects relevant 76 quarterly data for the period between year 2000 and 2018 from KRX(Korea Excange), Bank of Korea, and FnGuide. The Regression analysis, Unit-root test, and Monte Carlo simulation are hired to analyze the data. Findings - First, most of the sample banks failed to keep 8% BIS ratio for the adverse and severely Adverse Scenarios, implying that Korean banks must make every effort to realize better BIS ratios under adverse market conditions. Second, we suggest the better Monte Carlo simulation model for the Korean banks by finding that the more appropriate volatility should be different depending on variables rather than simple two-sigma which has been used in the previous studies. Third, we find that the stepwise regression model is better fitted than simple regression model in forecasting macro-economic variables for the BIS variables. Fourth, we find that, for the more robust and significant statistical results in designing stress tests, Korean banks are required to construct more valid time-series and cross-sectional data-base. Research implications or Originality - The above results all together show that the optimal volatility in designing optimal Monte Carlo simulation varies depending on the country, and many Korean banks fail to pass sress test under the adverse and severely adverse scenarios, implying that Korean banks need to make improvement in the BIS ratio.

• Journal of Practical Agriculture & Fisheries Research
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• v.19 no.1
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• pp.109-117
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• 2017

In the production of organic Panax ginseng, the morphological changes were confirmed by providing general water and microbubble water, respectively. Analysis of seedling ginseng treated with general water and bubbles water revealed that many seedlings were formed in the seedling treated with bubble water, and about 15% weight increase occurred in the growing period. The growth rate of stem, leaf, and root was about 15% higher than that of all. Taken together, the growth of seedling cultivation using bubble water was about 15% overall. In order to process ginseng, the dried ginseng was higher in dry weight than the general water seedling seedlings grown in bubble water. This suggests that more processed products will be produced per unit weight at the time of producing the processed products at the farm, which can directly increase the farm income.

• Korea Trade Review
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• v.47 no.4
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• pp.215-231
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• 2022

Analysis and forecasting of the Baltic Capsize Index (BCI) is important for managing an entity's losses and risks from the uncertainty and volatility of the fast-changing maritime transport market in the future. This study conducted volatility transition analysis through the GARCH model, using BCI which is highly related to steel raw materials. As for the data, 2,385 monthly data were used from March 1999 to March 2021. In this study, after basic statistical analysis, unit root and cointegration test, the GARCH, EGARCH, and DCC-GARCH models were used for volatility transition analysis. As the results of GARCH and EGARCH model, we confirmed that all variables had no autocorrelation between the standardized residuals for error terms and the square of residuals, that the variability of all variables at this time was likely to persist in the future, and that the variability of the time-series error term impact according to Iron ore trade (IoT). In addition, through the EGARCH model, the magnitude convenience of all variables except the Iron ore price (IOP) and Capesize bulk fleet (BCF) variables was greater than the positive value (+). As a result of analyzing the DCC-GARCH (1,1) model, partial linear combinations were confirmed over the entire period. Estimating the effect of variability transition on BCF and C5 with statistically significant linear combinations with BCI confirmed that the impact of BCF on BCI was greater than the impact of BCI itself.