• Journal of The Korean Association For Science Education
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• v.16 no.1
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• pp.61-76
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• 1996

The purpose of this study was to apply the instructional strategies for conceptual change prescribed by Posner et al(1982) to the astronomic content domain taught in the elementary and middle school and to analyze the characteristics of students' knowledge revealed in the test before, during and after the instruction. Also, it was to investigate the intercorrelation of cognitive levels, spatial ability and science achievement. The major findings of this study are as follows: 1. Students had a great variety of misconceptions related to the motion of the moon before the instruction, that is, the phases, the names of phases and the cause of changing phases by the moon's orbit about the earth, the moon's appearance and location at the given time, the relative positions of earth, moon and sun during a lunar eclipse, the cause that a full moon is not at the line of node once a month. In the analysis of students' responses concerning the cause of changing phases of the moon and a lunar eclipse, the results indicate that the great majority of students had rote learning rather than meaningful learning in the middle school. 2. Students' reponses during the instruction concerning the changing phases of the moon and the predictive knowledge about the motions of the earth and the moon were analyzed. 1) According to the results of the test given before and after experiment, after discussion, achievement score of the whole of subjects and groups in both preformal and formal cognitive levels appeared to increase linearly. 2) There was no statistically significant differences of achievement scores before and after experiment, after discussion between preformal group and formal group in cognitive levels. 3. Distribution of achievement scores according to the whole of subjects and groups in preformal and formal cognitive levels shows that there was a statistically significant difference between pretest and posttest. 4. Types of conceptual changes concerning the cause of changing phases of the moon that occurred from pretest to posttest were classified as accommodation, incomplete accommodation, assimilation, no change and no model. Six of the seven students starting instruction with alternative frameworks didn't sustain those alternative models throughout instruction. Five of these six students accommodated completely and the last one partially. Seventy-nine percentage of students taking instruction with fragmental models assimilated correct propositions at the end of the instruction. These results suggest that conceptual change model prescribed by Posner et al(1982) has promised the meaningful learning to students taking with fragmental models, especially in cases where students with misconception enter instruction. 5. High correlation between achievement score of simple-recall items and that of written items in pretest and posttest indicates that the higher students got the score in simple-recall items the better they also performed in written items. However, there was no statistically significant differences among cognitive levels, spatial ability and science achievement in the whole of subjects and groups according to the cognitive levels.

• Korean Journal of Radiology
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• v.25 no.2
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• pp.189-198
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• 2024

Objective: To investigate the prognostic utility of radiomics features extracted from ¹⁸F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). Materials and Methods: A total of 126 adults with ENKTCL who underwent ¹⁸F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. Results: Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, β2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. Conclusion: The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of Bio-Environment Control
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• v.29 no.2
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• pp.141-152
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• 2020

The rapid on-site measurement of hydroponic nutrients allows for the more efficient use of crop fertilizers. This paper reports on the development of an embedded on-site system consisting of multiple ion-selective electrodes (ISEs) for the real-time measurement of the concentrations of macronutrients in hydroponic solutions. The system included a combination of PVC ISEs for the detection of NO₃, K, and Ca ions, a cobalt-electrode for the detection of H₂PO₄, a double-junction reference electrode, a solution container, and a sampling system consisting of pumps and valves. An Arduino Due board was used to collect data and to control the volume of the sample. Prior to the measurement of each sample, a two-point normalization method was employed to adjust the sensitivity followed by an offset to minimize potential drift that might occur during continuous measurement. The predictive capabilities of the NO₃ and K ISEs based on PVC membranes were satisfactory, producing results that were in close agreement with the results of standard analyzers (R² = 0.99). Though the Ca ISE fabricated with Ca ionophore II underestimated the Ca concentration by an average of 55%, the strong linear relationship (R² > 0.84) makes it possible for the embedded system to be used in hydroponic NO₃, K, and Ca sensing. The cobalt-rod-based phosphate electrodes exhibited a relatively high error of 24.7±9.26% in the phosphate concentration range of 45 to 155 mg/L compared to standard methods due to inconsistent signal readings between replicates, illustrating the need for further research on the signal conditioning of cobalt electrodes to improve their predictive ability in hydroponic P sensing.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.25 no.1
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• pp.28-36
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• 2014

Objectives : The goal of this study was to examine the association between zinc and lead level and symptoms of attention-deficit hyperactivity disorder (ADHD) among Korean children. Methods : A total of 89 clinic-referred children participated in the study (ADHD group=45, control group=44). The participants were 5-15 years old, and were mainly from urban areas of Seoul, Korea. ADHD was diagnosed using the Kiddie-Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version. We excluded children with a comorbid psychiatric disorder, medical illness requiring medication, or a prior history of taking ADHD medication. In order to evaluate the severity of ADHD symptoms, parents' Korean ADHD Rating Scale (K-ARS) was used. The ADHD diagnostic system (ADS) was used for evaluation of the severity of inattention and impulsivity. All participants completed the intelligence test and hair mineral analysis. Multiple regression analysis was used to examine the effect of hair zinc and lead levels on the K-ARS and ADS. We measured the predictive ability of the zinc and lead levels using logistic regression analysis. Results : The lead level explained the score for omission errors, commission errors, and response time SD in visual ADS in the ADHD group (adjusted $R^2$=.243, p<.01, adjusted $R^2$=.362, p<.01, and adjusted $R^2$=.275, p<.01), the score for omission errors of auditory ADS in ADHD group (adjusted $R^2$=.407, p<.01) and the entire group (adjusted $R^2$=.292, p<.01). Zinc was significantly explanatory for the K-ARS scores for the entire group (adjusted $R^2$=.248, p<.001) and the ADHD group (adjusted $R^2$=.247, p<.05). Conclusion : These findings suggest a possible role of zinc and lead in ADHD. Lead concentration in hair samples affected the ADS scores, and this was more prominent in children with ADHD. Children with ADHD had a lower zinc concentration in their hair, and the zinc concentration in hair showed negative correlation with the K-ARS score.

• Journal of Korean Academy of Nursing
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• v.25 no.4
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• pp.617-632
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• 1995

Nowadays, most big hospitals have a computer system to manage their administration. For maxi mum effectiveness in managing the computer system, an analysis of the variables affecting its implementation is necessary from the beginning. This study was done to analyze the variables influencing the operation of a hospital information system (HIS). The theoretical base for this study considered the combined effects of user expectations of computerization, and computer-anxiety. The relationship between variables in the theoretical base were analyzed and the individual characteristics influencing each variable were also analyzed. This study was done in two steps. First, 344 nurses were given an initial questionnaire developed to evaluate the reliability of the items. Based on the results, a second revised questionnaire was administered to 88 nurses who had been working in the areas where HIS was applied. The results of the first and second steps of the study are as follows 1. The initial study was done with nurses who were trained on the computer system briefly before HIS was implemented. The individual characteristics influencing computer anxiety and expectation regarding computer system usage in that initial study included, length of career, type of degree or certification, previous experiences with a computer, training on a computer, desire for computer training, and level of acceptance of a computerized work environment. But in the second study with nurses working in areas of the hospital where HIS was introduced, the work site was the only influencing characteristics. There-fore, in applying a computer system, overcoming work-environment barriers will be more import-ant than any individual characteristics. 2. The computer anxiety of the nurses in both groups, before and after the computer system ap-plication, was below the average level but the expectation of the effects of computerization was above average. The nurses using the computer program showed an above average level of satis-faction with the computer system itself, and with its effect on their efficiency. Therefore, the ability of nurses operating HIS will be positively. predictive. 3. For the variables included in the theoretical framework of the study, all of the correlational coefficients were statistically significant in the analysis of variation correlation. Therefore, the theoretical base of the study, "expectation in con junction with computer anxiety" can be considered an model which can be evaluated. Accord-ing to our analysis, the higher the level of nurses' motivation to use the computer system and the lower the anxiety about computer usage, the higher the possibility of computer system acceptance by nurses. The results of this study showed that in applying a computer system in the hospital, the main characteristic influencing acceptance was where the individual worked rather than personal characteristics such as length of career, type of degree or certification, and previous experiences with a computer. Therefore, it is suggested that the first step in uncovering and eliminating hindrance factors in ap-plication of a computer system should be an analysis of working conditions in relation to the functional content of the computer system. The suitability of the theoretical model based on the hypothesis ap-plied in this study should be further tested.

• Korean Journal of Childcare and Education
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• v.10 no.4
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• pp.159-176
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• 2014

This study aims to examine the suggestions by studies that family predictors are related to developmental outcomes more strongly for children reared principally by their parents compared to those with extensive child care experience. Zero-order correlations between family predictors and developmental outcomes were conducted and the significance of differences in correlation coefficients between the two child care groups were examined. There was no evidence that there exist systematic differences between the two groups in predictive power of family factors except a few exceptions. At 2 years, social parenting style was more strongly associated with communication ability in the extensive child care group. At 3 years, some HOME subscales were related to a child's expressive and receptive vocabulary skills in significantly greater magnitudes in the mother care group. The findings also implied the potential contribution of child care environment on developmental outcomes for those who spend extensive hours in nonmaternal care.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.3
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• pp.185-193
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• 2007

This review focuses on the use of $^{18}F-FDG$ PET/CT to evaluate second primary cancers. The emergence of a second primary cancer is an important prognostic factor in cancer patients. The early detection of a second primary cancer and the appropriate treatment are essential for reducing the morbidity and mortality associated with these tumors. Integrated $^{18}F-FDG$ PET/CT, which can provide both the metabolic and anatomic information of a cancer, has been shown to have a better accuracy in oncology than either CT or conventional PET. The whole body coverage and high sensitivity of $^{18}F-FDG$ PET/CT along with its ability to provide both metabolic and anatomic information of a cancer make it suitable for evaluating a second primary cancer in oncology. Whole body $^{18}F-FDG$ PET/CT is useful for screening second primary cancers with a high sensitivity and good positive predictive value. In order to rule out the presence of a second primary cancer or an unexpected metastasis, further diagnostic work-up is essential when abnormal findings indicative of a second primary cancer are found on the PET/CT images. PET/CT is better in detecting a second primary tumor than conventional PET.

• The Journal of the Korean bone and joint tumor society
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• v.2 no.1
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• pp.1-7
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• 1996

PURPOSE : The purpose of this study is to know the ability of detecting malignant tumor tissue by Tl-201 scan, and to compare with that of Tc-99m-MIBI and Tc-99m-MDP scan. MATERIAL AND METHODS : Between February 1994 and December 1995,38 unselected patients with various bone pathologies were studied prospectively. Eighteen had malignant bone and soft tissue pathologies, while twenty had benign. All patients were studied with Tl-201, Tc-99mMIBI and Tc-99m-MDP scan prior to surgical biopsy. PICKER Prism 2000 gamma camera with high resolution parallel hole collimator was used for scanning. To avoid the interaction of isotope, the early(30min.) and delayed phase(3hrs.) of Tl-20l scan was performed first and Tc-99m-MIBI scan was performed after 30 minutes, and then Tc-99m-MDP scan 48 hours later. The scan images were visually evaluated by a blinded nuclear medicine physician. We could find true positive, true negative, false positive and false negative by the comparison of results with those of biopsy. We calculated positive and negative predictive value(%), sensitivity(%), specificity(%) and diagnostic accuracy(%) of each scan. RESULT : The results of each scan were 85.7, 100, 100, 85, 92.1% in Tl-201, 81, 94.1, 94.4, 80, 86.8% in Tc-99m-MIBI and 50, 66.7, 88.9, 20, 52.6% in Tc-99m-MDP scan. As a conclusion, Tl-201 scan was the most specific and accurate method for detecting malignant tumor tissue. Tc-99m-MIBI scan was also good for malignant tumor searching. CONCLUSION : With our results, we can use Tl-201 scan to differentiate benign from malignant tumor, and to evaluate the response of preoperative chemotherapy or radiotherapy, and to determine the residual tumor or local recurrence. For the better result, we need to have a more detail information about false positive cases and a more objective and quantitative reading technique.