• Journal of the Korea Society for Simulation
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• v.33 no.1
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• pp.1-17
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• 2024

Forest management is known to beneficially influence stand structure and wood production, yet quantitative understanding as well as an illustrative depiction of the effects of different management approaches on tree growth and stand dynamics are still scarce. Long-term management of beech forests must balance public interests with ecological aspects. Efficient forest management requires the reliable prediction of tree growth change. We aimed to develop a novel hybrid simulation approach, which realistically simulates short- as well as long-term effects of different forest management regimes commonly applied, but not limited, to German low mountain ranges, including near-natural forest management based on single-tree selection harvesting. The model basically consists of three modules for (a) natural seedling regeneration, (b) mortality adjustment, and (c) tree growth simulation. In our approach, an existing validated growth model was used to calculate single year tree growth, and expanded on by including in a newly developed simulation process using calibrated modules based on practical experience in forest management and advice from the local forest. We included the following different beech forest-management scenarios that are representative for German low mountain ranges to our simulation tool: (1) plantation, (2) continuous cover forestry, and (3) reserved forest. The simulation results show a robust consistency with expert knowledge as well as a great comparability with mid-term monitoring data, indicating a strong model performance. We successfully developed a hybrid simulation that realistically reflects different management strategies and tree growth in low mountain range. This study represents a basis for a new model calibration method, which has translational potential for further studies to develop reliable tailor-made models adjusted to local situations in beech forest management.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.169-174
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• 2024

This study purports to identify the factors that contribute to the classification of age groups or generations of Koreans. Independent variables such as respondents' attitudes toward welfare, attitudes toward equity, education level, perception of inequality in Korean society, tax awareness, and health status are included in the model that were put into the analysis with the main interest. Since this study does not construct any hypothesis prior to analysis, the nature of this study can be said exploratory. The data utilized for the analysis are from the 17th year of the Korean Welfare Panel collected in 2022, and a linear discrimination analysis technique will be used. First and foremost, a theoretical review of the generational classification will be conducted through domestic and international literature in the past. To date, there is no quantitative studies in Korea that have a significant influence on the generational classification. Therefore, in this study, a theoretical review of political tendencies and values, which are estimated to have a significant influence on the generational classification, that is, the difference between generations, will be significant. The perception and attitude toward welfare will be discussed in the review of values. Next, analysis models, analysis techniques, and variables to be used in the analysis will be introduced. After

• The Korean Journal of Pesticide Science
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• v.14 no.4
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• pp.319-325
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• 2010

To understand the influences of the substituents ($R_1{\sim}R_4$) on insecticidal activity of N'-phenyl-N-methylformamidine analogues (1~22) against two spotted spider mite (Tetranychus urticae), comparative molecular field analysis (CoMFA) model and comparative molecular similarity indices analysis (CoMSIA) model as three dimensional quantitative structure-activity relationships (3D-QSARs) model were derived and discussed quantitatively. From the results, the correlativity and predictability ($r^2{_{cv.}}=0.575$ and $r^2{_{ncv.}}=0.945$) of the CoMFA 1 model were higher than those of the rest models. The the CoMFA 1 and CoMSIA 1 model with the sensitivity of the perturbation and the prediction produced ($d_q{^{2'}}/dr^2{_{yy}}=1.071{\sim}1.146$ & $q^2=0.545{\sim}0.626$) by a progressive scrambling analysis were not dependent on chance correlation. The insecticidal activities from the optimized CoMFA 1 model were depend upon the steric field (62.5%), electrostatic field (28.9%), and hydrophobic field (8.6%) of N'-phenyl-N-methylformamidine analogues. Therefore, the inhibitory activities with optimized CoMFA 1 model were dependent upon steric factor. From the contour maps of the optimized models, it is predicted that the structural distinctions that contribute to the insecticidal activity will be able to applied new potent insecticides design.

• Journal of Bio-Environment Control
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• v.24 no.3
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• pp.161-166
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• 2015

To determine the adequate levels of light intensity and $CO_2$ concentration for mango grown in greenhouses, quantitative measurements of photosynthetic rates at various leaf positions in the tree are required. The objective of this study was to develop two-variable leaf photosynthetic models of Irwin mango (Mangifera indica L. cv. Irwin) using light intensity and $CO_2$ concentration at different leaf positions. Leaf photosynthetic rates at different positions (top, middle, and bottom) were measured by a leaf photosynthesis analyzer at light intensities (0, 50, 100, 200, 300, 400, 600, and $800{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$) with $CO_2$ concentrations (100, 400, 800, 1200, and $1600{\mu}mol{\cdot}mol^{-1}$). The two-variable model consisted of the two leaf photosynthetic models expressed as negative exponential functions for light intensity and $CO_2$ concentrations, respectively. The photosynthetic rates of top leaves were saturated at a light intensity of $400{\mu}mol{\cdot}^{-2}{\cdot}s^{-1}$, while those of middle and bottom leaves saturated at $200{\mu}mol{\cdot}^{-2}{\cdot}s^{-1}$. The leaf photosynthetic rates did not reach the saturation point at a $CO_2$ concentration of $1600imolmol^{-1}$. In validation of the model, the estimated photosynthetic rates at top and bottom leaves showed better agreements with the measured ones than the middle leaves. It is expected that the optimal conditions of light intensity and $CO_2$ concentration can be determined for maximizing photosynthetic rates of Irwin mango grown in greenhouses by using the two-variable model.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.4
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• pp.415-423
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• 2010

Pore network models are useful tools to investigate soil pore geometry. These models provide quantitative information of pore geometry from 3D images. This study presents a pore network model to quantify pore structure and hydraulic characteristics. The objectives of this work were to apply the pore network model to characterize pore structure from large images to quantify pore structure, calculate water retention and hydraulic conductivity properties from a three dimensional soil image, and to combine measured hydraulic properties from experiments with calculated hydraulic properties from image. Soil samples were taken from a site located at the Baltimore science center, which is located inside of the city. Undisturbed columns were taken from the site and scanned with a computer tomographer at resolutions of 22 ${\mu}m$. Pore networks were extracted by medial-axis transformation and were used to measure pore geometry from one of the scanned samples. Water retention and unsaturated hydraulic conductivity values were calculated from the soil image. Properties of soil bulk density, water retention and unsaturated hydraulic conductivity were measured from three replicates of scanned soil samples. 3D image analysis provided accurate detailed pore properties such as individual pore volumes, pore length, and tortuosity of all pores. These data made possible to calculate accurate estimations of water retention and hydraulic conductivity. Combination of the calculated and measured hydraulic properties gave more accurate information on pore sizes over wider range than measured or calculated data alone. We could conclude that the hydraulic property computed from soil images and laboratory measurements can describe a full structure of intra- and inter-aggregate pores in soil.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.15 no.3
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• pp.127-137
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• 2015

Purpose: The main aim of this study was to compare and analyze expression profiles of microRNAs (miRNAs) to establish miRNA signature of Fabry nephropathy related epithelial mesenchymal transition (EMT). Methods: Expression profiles of miRNAs in kidney tissue samples and cell lines from normal and Fabry disease mouse model were examined by miRNA expression microarray analysis followed by quantitative real-time polymerase chain reaction analysis (qRT-PCR). Results: In the miRNA expression microarray analysis of Fabry mouse kidney tissues compared to wild type mouse, 5 and 3 miRNAs among 1,247 miRNAs examined were up- and down-regulated, respectively. Among them, miR-149-5p was down-regulated about 2-fold in Fabry kidney samples. The down-regulations of miR-149-5p were observed in kidney tissues of under 35 week-old-Fabry mice. However, this down-regulation was not observed in kidney tissues of 42 week-old Fabry mice. In SV40 MES 13 cells, mouse mesangial cells, treated with globotriaosylsphingosine (lyso-Gb3), miR-149-5p was also downregulated. The down-regulation of miR-149-5p induced up-regulation of its target genes related to EMT. Conclusion: The miRNA expression array and qRT-PCR results show that miR-149-5p expression was decreased in kidney tissues of Fabry mice compared to wild type mice under 35 weeks of age. Along with the observation of miR-149-5p expression in Fabry disease cell models, these results indicate that the down-regulated miR-149-5p were related to the biological response of mesangial cells to lyso-Gb3 and also have influence to the transcriptional up-regulation of its target genes. These results suggest miR-149-5p might play important roles in the Fabry nephropathy.

• Journal of Food Hygiene and Safety
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• v.35 no.1
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• pp.51-59
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• 2020

This study evalutated the risk of foodborne illness from Vibrio spp. (Vibrio vulnificus and Vibrio cholerae) through sea squirt consumption. The prevalence of V. vulnificus and V. cholerae in sea squirt was evaluated, and the predictive models to describe the kinetic behavior of the Vibrio in sea squirt were developed. Distribution temperatures and times were collected, and they were fitted to probabilistic distributions to determine the appropriate distributions. The raw data from the Korea National Health and Nutrition Examination Survey 2016 were used to estimate the consumption rates and amount of sea squirt. In the hazard characterization, the Beta-Poisson model for V. vulnificus and V. cholerae infection was used. With the collected data, a simulation model was prepared and it was run with @RISK to estimate probabilities of foodborne illness by pathogenic Vibrio spp. through sea squirt consumption. Among 101 sea squirt samples, there were no V. vulnificus positive samples, but V. cholerae was detected in one sample. The developed predictive models described the fates of Vibrio spp. in sea squirt during distribution and storage, appropriately shown as 0.815-0.907 of R² and 0.28 of RMSE. The consumption rate of sea squirt was 0.26%, and the daily consumption amount was 68.84 g per person. The Beta-Poisson model [P=1-(1+Dose/β)^-α] was selected as a dose-response model. With these data, a simulation model was developed, and the risks of V. vulnificus and V. cholerae foodborne illness from sea squirt consumption were 2.66×10^-15, and 1.02×10^-12, respectively. These results suggest that the risk of pathogenic Vibrio spp. in sea squirt could be considered low in Korea.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.2
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• pp.61-73
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• 2021

This study aimed to identify the landscape perception and landscape characteristics of users by utilizing SNS data generated by their experiences. Therefore, how to recognize the main places and scenery appearing on the island, and what are the characteristics of the main scenery were analyzed using online text data and photo data. Text data are text mining and network structural analysis, while photographic data are landscape identification models and color analysis. As a result of the study, First, as a result of frequency analysis of Bakji·Banwol-do topics, we were able to derive keywords for local landscapes such as 'Purple Bridge', 'Doori Village', and location, behavior, and landscape images by analyzing them simultaneously. Second, the network structure analysis showed that the connection between key and undrawn keywords could be more specifically analyzed, indicating that creating landscapes using colors is affecting regional activation. Third, after analyzing the landscape identification model, it was found that artificial elements would be excluded to create preferred landscapes using the main targets of "Purple Bridge" and "Doori Village", and that it would be effective to set a view point of the sea and sky. Fourth, Bakji·Banwol-do were the first islands to be created under the theme of color, and the colors used in artificial facilities were similar to the surrounding environment, and were harmonized with contrasting lighting and saturation values. This study used online data uploaded directly by visitors in the landscape field to identify users' perceptions and objects of the landscape. Furthermore, the use of both text and photographic data to identify landscape recognition and characteristics is significant in that they can specifically identify which landscape and resources they prefer and perceive. In addition, the use of quantitative big data analysis and qualitative landscape identification models in identifying visitors' perceptions of local landscapes will help them understand the landscape more specifically through discussions based on results.

• Asia Marketing Journal
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• v.14 no.1
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• pp.83-98
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• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.