• Information Systems Review
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• v.11 no.2
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• pp.23-44
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• 2009

U-Services are inevitably essential for the realization of u-Cities. Most local governments in Korea have expressed much interest in introducing u-Cites and related u-Services. Since researchers anticipate that developing u-Cities will produce economic effects, the Korea government has support local governments to develop u-Cities and necessary u-Services. However, the technology issues have been dominiated in the field of U-City services and most of the U-City services do not reflects all the complicated and pluralistic sides of environment, which are caused by future uncertainties in developing u-Cites. For the purpose of addressing the above uncertainties, this paper attempts to develop the possible scenarios for U-City services through a scenario planning approach. A focus group interview and survey with professionals in the field of planning u-Cities was performed to identify these uncertainties. Then, in order to investigate the validity of the scenario planning methodology, the u-Service "u-Water purity monitoring" is adopted. After considering the relevant issues, we developed two possible scenarios: a mutual linkage service among u-Service related organization and a cooperating and coordinating service among local governments. On the basis of these scenarios, the strategies for potential U-City services are formulated. Various participants in developing U -City services are encouraged to use the scenarios as the foundation of predicting future features of u-Cities and developing the framework of the U-City service scenarios effectively.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.95-107
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• 2023

The purpose of this study is to investigate the impact of export promotion programs (EPPs) on the export performance of small- and medium-sized enterprises (SMEs), with a specific focus on the influence of EPPs for promising SMEs in the export market. Using data on SMEs provided by the Industrial Bank of Korea (IBK), we conducted a fixed-effects model analysis from 2016 to 2019. Our study shows that EPPs have a positive and significant relationship with export intensity. Further analysis reveals that SMEs utilizing the financing support system provided by EPPs tend to improve their export growth and financial performance relative to their counterparts. While EPPs can assist SMEs with their internationalization efforts, their similarity and redundancy are recognized as potential limitations. This study complements the existing literature that has mainly focused on surveys and cross-sectional analysis by specifying the research subject to promising SMEs in export, and analyzing the effects of the export promotion program supported by IBK Industrial Bank. The results of this study are expected to provide implications for improving SMEs' export capabilities.

• Korean journal of applied entomology
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• v.48 no.2
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• pp.123-158
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• 2009

An attempt was made to stimulate future research by providing exemplary information, which would integrate published knowledge to solve specific pest problem caused by resistance. This review was directed to find a way for delaying resistance development with consideration of chemical(s) nature, of mixture, rotation, or mosaics, and of insecticide(s) compatible with the biological agents in integrated pest management (IPM). The application frequency, related to the resistance development, was influenced by insecticide activity from potentiation, residual period, and the vulnerability to resistance development of chemical, with secondary pest. Chemical affected feeding, locomotion, flight, mating, and predator avoidance. Insecticides with negative cross-resistance by the difference of target sites and mode of action would be adapted to mixture, rotation and mosaic. Mixtures for delaying resistance depend on each component killing very high percentage of the insects, considering allele dominance, cross-resistance, and immigration and fitness disadvantage. Potential disadvantages associated with mixtures include disruption of biological control, resistance in secondary pests, selecting very resistant population, and extending cross-resistance range. The rotation would use insecticides in high and low doses, or with different metabolic mechanisms. Mosaic apply insecticides to the different sectors of a grid for highly mobile insects, spray unrelated insecticides to sedentary aphids in different areas, or mix plots of insecticide-treated and untreated rows. On the evolution of pest resistance, selectivity and resistance of parasitoids and predator decreased the number of generations in which pesticide treatment is required and they could be complementary to refuges from pesticides To enhance the viability of parasitoids, the terms on the insecticides selectivity and factors affecting to the selectivity in field were examined. For establishment of resistant parasitoid, migration, survivorship, refuge, alternative pesticides were considered. To use parasitoids under the pressure of pesticides, resistant or tolerant parasitoids were tested, collected, and/or selected. A parasitoid parasitized more successfully in the susceptible host than the resistant. Factors affecting to selective toxicity of predator are mixing mineral oil, application method, insecticide contaminated prey, trait of individual insecticide, sub-lethal doses, and the developmental stage of predators. To improve the predator/prey ratio in field, application time, method, and formulation of pesticide, reducing dose rate, using mulches and weeds, multicropping and managing of surroundings are suggested. Plant resistance, predator activity, selective insect growth regulator, and alternative prey positively contributed to the increase of the ratio. Using selective insecticides or insecticide resistant predator controlled its phytophagous prey mites, kept them below an economic level, increased yield, and reduced the spray number and fruits damaged.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of Hospice and Palliative Care
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• v.22 no.4
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• pp.145-155
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• 2019

Purpose: Amendment to the Act on Decisions on Life-sustaining Treatment was recently enacted to designate long-term care hospitals as providers of hospice and palliative care. Despite its benefit of providing improved accessibility to end-of-life care, the amendment has raised concerns about its effect on quality of service. This study aimed to use information obtained from an expert group interview and previous studies to compare how cancer patients, family caregivers, physicians, and the general Korean population perceive the potential benefits and risks of this amendment. Methods: We conducted a multicenter cross-sectional study from July to October 2016. The included participants answered a structured questionnaire regarding the extent to which they agree or disagree with the questionnaire items indicating the potential benefits and risks of the amendment. Chi-square tests and univariate and multivariate logistic regression analyses were performed. Results: Compared with the general population, physicians agreed more that long-term care hospitals are currently not adequately equipped to provide quality hospice and palliative care. Family caregivers found improved access to long-term care hospitals more favorable but were more likely to agree that these hospitals might prioritize profits, thereby threatening the philosophy of hospice care, and that families might cease to fulfill filial responsibilities. Compared with the general population, cancer patients were more concerned about the potentially decreased service quality in this setting. Conclusion: Although potential service beneficiaries and providers expected improved accessibility of hospice and palliative care services, they were also concerned whether the system can provide adequate quality of end-of-life care.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Environmental Mutagens and Carcinogens
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• v.22 no.3
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• pp.142-148
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• 2002

Although ionizing radiation (IR) has been used to treat the various human cancers, IR is cytotoxic not only to cancer cells but to the adjacent normal tissue. Since normal tissue complications are the limiting factor of cancer radiotherapy, one of the major concerns of IR therapy is to maximize the cancer cell killing and to minimize the toxic side effects on the adjacent normal tissue. As an attempt to develop a method to monitor the degree of radiation exposure to normal tissues during radiotherapy, we investigated the transcriptional responses of human peripheral blood lymphocytes (PBL) following IR using cDNA microarray chip containing 1,221 (1.2 K) known genes. Since conventional radiotherapy is delivered at about 24 h intervals at 180 to 300 cGy/day, we analyzed the transcriptional responses ex-vivo irradiated human PBL at 200 cGy for 24 h-period. We observed and report on 1) a group of genes transiently induced early after IR at 2 h, 2) of genes induced after IR at 6 h, 3) of genes induced after IR at 24 h and on 4) a group of genes whose expression patters were not changed after IR. Since Biological consequences of IR involve generation of various reactive oxygen species (ROS) and thus oxidative stress induced by the ROS is known to damage normal tissues during radiotherapy, we further tested the temporal expression profiles of genes involved in ROS modulation by RT-PCR. Specific changes of 6 antioxidant genes were identified in irradiated PBL among 9 genes tested. Our results suggest the potential of monitoring post-radiotherapy changes in temporal expression profiles of a specific set of genes as a measure of radiation effects on normal tissues. This type of approach should yield more useful information when validated in in vivo irradiated PBL from the cancer patients.

• Nutrition Research and Practice
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• v.8 no.3
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• pp.336-341
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• 2014

BACKGROUND/OBJECTIVE: It is expected that dairy products such as cheeses, which are the main source of cholesterol and saturated fat, may lead to the development or increase the risk of cardiovascular and metabolic diseases; however, the results of different studies are inconsistent. This study was conducted to assess the association between cheese consumption and cardiovascular risk factors in an Iranian adult population. SUBJECTS/METHODS: Information from the Isfahan Healthy Heart Program (IHHP) was used for this cross-sectional study with a total of 1,752 participants (782 men and 970 women). Weight, height, waist and hip circumference measurement, as well as fasting blood samples were gathered and biochemical assessments were done. To evaluate the dietary intakes of participants a validated food frequency questionnaire, consists of 49 items, was completed by expert technicians. Consumption of cheese was classified as less than 7 times per week and 7-14 times per week. RESULTS: Higher consumption of cheese was associated with higher C-Reactive Protein (CRP), apolipoprotein A and high density lipoprotein cholesterol (HDL-C) level but not with fasting blood sugar (FBS), total cholesterol, low density lipoprotein cholesterol (LDL-C), triglyceride (TG) and apolipoprotein B. Higher consumption of cheese was positively associated with consumption of liquid and solid oil, grain, pulses, fruit, vegetable, meat and dairy, and negatively associated with Global Dietary Index. After control for other potential confounders the association between cheese intake and metabolic syndrome (OR: 0.81; 96%CI: 0.71-0.94), low HDL-C level (OR: 0.87; 96%CI: 0.79-0.96) and dyslipidemia (OR: 0.88; 96%CI: 0.79-0.98) became negatively significant. CONCLUSION: This study found an inverse association between the frequency of cheese intake and cardiovascular risk factors; however, further prospective studies are required to confirm the present results and to illustrate its mechanisms.

• The Plant Pathology Journal
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• v.36 no.4
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• pp.305-313
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• 2020

Host shifting and host expansion of fungal plant pathogens increases the rate of emergence of new pathogens and the incidence of disease in various crops, which threaten global food security. Magnaporthe species cause serious disease in rice, namely rice blast disease, as well as in many alternative hosts, including wheat, barley, and millet. A severe outbreak of wheat blast due to Magnaporthe oryzae occurred recently in Bangladesh, after the fungus was introduced from South America, causing great loss of yield. This outbreak of wheat blast is of growing concern, because it might spread to adjacent wheat-producing areas. Therefore, it is important to understand the host range and population structure of M. oryzae and related species for determining the evolutionary relationships among Magnaporthe species and for managing blast disease in the field. Here, we collected isolates of M. oryzae and related species from various Poaceae species, including crops and weeds surrounding rice fields, in Korea and determined their phylogenetic relationships and host species specificity. Internal transcribed spacer-mediated phylogenetic analysis revealed that M. oryzae and related species are classified into four groups primarily including isolates from rice, crabgrass, millet and tall fescue. Based on pathogenicity assays, M. oryzae and related species can infect different Poaceae hosts and move among hosts, suggesting the potential for host shifting and host expansion in nature. These results provide important information on the diversification of M. oryzae and related species with a broad range of Poaceae as hosts in crop fields.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6697-6704
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• 2015

Background: The burden of breast and cervical cancer is changing over time in developing countries. Regular screening is very important for early detection and treatment. In this study, we assessed inequalities in breast and cervical cancer screening rates in women according to household wealth status, and analyzed the potential predictors associated with a low cancer screening rate in Jordan. Materials and Methods: A nationwide populationbased cross-sectional survey collected information on different variables at the national level. All ever-married women (the phrase is used throughout the text to refer to women who had ever married) aged 15-49 years were included in the survey. Analysis of breast self-examination (BSE) and clinical breast examination (CBE) at least once in the previous year was carried out in 11,068 women, while lifetime Pap-smear testing was carried out in 8,333 women, aged 20-49 years. Results: Over 39% and 19% of ever-married Jordanian women reported having undergone a breast examination during the previous year and Pap smear examination at least once in their lifetime, respectively. The rate of BSE in the previous year was 31.5%, that of CBE in the previous year was 19.3%, and that of Pap smear examination at least once in life was 25.5%. The adjusted OR was higher for performing BSE (aOR 1.22, 95% CI 1.04-1.43), undergoing CBE (aOR 1.31, 95% CI 1.08-1.60) and undergoing Pap smear examination (aOR 2.38, 95% CI 1.92-2.93) among women in the highest wealth-index quintile as compared to those in the lowest quintile. The concentration index was 0.11 for BSE, 0.01 for CBE, and 0.27 for Pap smear examination. Women in their twenties, living in rural or the southern region of Jordan, with an elementary school education or less, who listened to the radio or read the newspaper not more than a few times a year, and nulliparous women were less likely to undergo breast and cervical cancer screening. Conclusions: The rates of breast and cervical cancer screening are low in Jordan. Reducing the sociodemographic and economic inequalities in breast and cervical cancer screenings requires concerted outreach activities for women living under socially deprived conditions.