• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.9-20
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• 2018

The purpose of this paper is to analyze the effect of corporation internal reservation on R&D investment. It is to find how much effect the reflux tax has achieved through empirical analysis. In addition, education training expense was taken as a moderating variable to find the effectiveness of government policy. Furthermore, the study looked through the effect once again by using GMB model. According to the result counted by regression analysis, it could be concluded that the effect of both moderation and intervention had a significant effect and the variable of interest cost and welfare & benefit cost in model 1, 2 and 3 had a meaningful impact at the level of 99%. On the other hand, the previous corporate internal reservation failed to show any significant result in all types of models. Even in GBM model of convergence level applied to additional analysis, similar results came out.

• Geomechanics and Engineering
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• v.14 no.4
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• pp.345-354
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• 2018

Currently, layered geogrid method (LGM) is the commonly practiced technique for reinforcement of slopes. In this paper the geogrid-box method (GBM) is introduced as a new approach for reinforcement of rock-soil slopes. To achieve the objectives of this study, a laboratory setup was designed and the slopes without reinforcements and reinforced with LGM and GBM were tested under the loading of a circular footing. The effect of vertical spacing between geogrid layers and box thickness on normalized bearing capacity and failure mechanism of slopes was investigated. A series of 3D finite element analysis were also performed using ABAQUS software to supplement the results of the model tests. The results indicated that the load-settlement behavior and the ultimate bearing capacity of footing can be significantly improved by the inclusion of reinforcing geogrid in the soil. It was found that for the slopes reinforced with GBM, the displacement contours are widely distributed in the rock-soil mass underneath the footing in greater width and depth than that in the reinforced slope with LGM, which in turn results in higher bearing capacity. It was also established that by reducing the thickness of geogrid-boxes, the distribution and depth of displacement contours increases and a longer failure surface is developed, which suggests the enhanced bearing capacity of the slope. Based on the studied designs, the ultimate bearing capacity of the GBM-reinforced slope was found to be 11.16% higher than that of the slope reinforced with LGM. The results also indicated that, reinforcement of rock-soil slopes using GBM causes an improvement in the ultimate bearing capacity as high as 24.8 times more than that of the unreinforced slope.

• Radiation Oncology Journal
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• v.37 no.1
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• pp.13-21
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• 2019

Purpose: Glioblastoma (GBM) carries a high propensity for in-field failure despite trimodality management. Past studies have failed to show outcome improvements with dose-escalation. Herein, we examined trends and outcomes associated with dose-escalation for GBM. Materials and Methods: The National Cancer Database was queried for GBM patients who underwent surgical resection and external-beam radiation with chemotherapy. Patients were excluded if doses were less than 59.4 Gy; dose-escalation referred to doses ≥66 Gy. Odds ratios identified predictors of dose-escalation. Univariable and multivariable Cox regressions determined potential predictors of overall survival (OS). Propensity-adjusted multivariable analysis better accounted for indication biases. Results: Of 33,991 patients, 1,223 patients received dose-escalation. Median dose in the escalation group was 70 Gy (range, 66 to 89.4 Gy). The use of dose-escalation decreased from 8% in 2004 to 2% in 2014. Predictors of escalated dose were African American race, lower comorbidity score, treatment at community centers, decreased income, and more remote treatment year. Median OS was 16.2 months and 15.8 months for the standard and dose-escalated cohorts, respectively (p = 0.35). On multivariable analysis, age >60 years, higher comorbidity score, treatment at community centers, decreased education, lower income, government insurance, Caucasian race, male gender, and more remote year of treatment predicted for worse OS. On propensity-adjusted multivariable analysis, age >60 years, distance from center >12 miles, decreased education, government insurance, and male gender predicted for worse outcome. Conclusion: Dose-escalated radiotherapy for GBM has decreased over time across the United States, in concordance with guidelines and the available evidence. Similarly, this large study did not discern survival improvements with dose-escalation.

• Molecules and Cells
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• v.37 no.1
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• pp.17-23
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• 2014

We already had reported that Bcl-w promotes invasion or migration in gastric cancer cells and glioblastoma multiforme (GBM) by activating matrix metalloproteinase-2 (MMP-2) via specificity protein 1 (Sp1) or ${\beta}$-cateinin, respectively. High expression of Bcl-w also has been reported in GBM which is the most common malignant brain tumor and exhibits aggressive and invasive behavior. These reports propose that Bcl-w-induced signaling is strongly associated with aggressive characteristic of GBM. We demonstrated that Sp1 protein or mRNA expression is induced by Bcl-w using Western blotting or RT-PCR, respectively, and markedly elevated in high-grade glioma specimens compared with low-grade glioma tissues using tissue array. However, relationship between Bcl-w-related signaling and aggressive characteristic of GBM is poorly characterized. This study suggested that Bcl-w-induced Sp1 activation promoted expression of glioma stem-like cell markers, such as Musashi, Nanog, Oct4 and sox-2, as well as neurosphere formation and invasiveness, using western blotting, neurosphere formation assay, or invasion assay, culminating in their aggressive behavior. Therefore, Bcl-w-induced Sp1 activation is proposed as a putative marker for aggressiveness of GBM.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.125-135
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• 2022

This study aims to explore variables using machine learning and provide analysis techniques suitable for predicting pharmacy sales whether government statistical indicators built to create an industrial ecosystem based on data, network, and artificial intelligence affect pharmacy sales. Therefore, this study explored predictive variables and performance through machine learning techniques such as Random Forest, XGBoost, LightGBM, and CatBoost using analysis data from January 2016 to December 2021 for 28 government statistical indicators and pharmacies in the retail sector. As a result of the analysis, economic sentiment index, economic accompanying index circulation change, and consumer sentiment index, which are economic indicators, were found to be important variables affecting pharmacy sales. As a result of examining the indicators MAE, MSE, and RMSE for regression performance, random forests showed the best performance than XGBoost, LightGBM, and CatBoost. Therefore, this study presented variables and optimal machine learning techniques that affect pharmacy sales based on machine learning results, and proposed several implications and follow-up studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2016-2029
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• 2023

Accurate prediction of critical illness is significant for ensuring the lives and health of patients. The selection of indicators affects the real-time capability and accuracy of the prediction for critical illness. However, the diversity and complexity of these indicators make it difficult to find potential connections between them and critical illnesses. For the first time, this study proposes an indicator analysis model to extract key indicators from the preoperative and intraoperative clinical indicators and laboratory results of critical illnesses. In this study, preoperative and intraoperative data of heart failure and respiratory failure are used to verify the model. The proposed model processes the datum and extracts key indicators through four parts. To test the effectiveness of the proposed model, the key indicators are used to predict the two critical illnesses. The classifiers used in the prediction are light gradient boosting machine (LightGBM) and eXtreme Gradient Boosting (XGBoost). The predictive performance using key indicators is better than that using all indicators. In the prediction of heart failure, LightGBM and XGBoost have sensitivities of 0.889 and 0.892, and specificities of 0.939 and 0.937, respectively. For respiratory failure, LightGBM and XGBoost have sensitivities of 0.709 and 0.689, and specificity of 0.936 and 0.940, respectively. The proposed model can effectively analyze the correlation between indicators and postoperative critical illness. The analytical results make it possible to find the key indicators for postoperative critical illnesses. This model is meaningful to assist doctors in extracting key indicators in time and improving the reliability and efficiency of prediction.

• Convergence Security Journal
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• v.22 no.4
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• pp.17-24
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• 2022

Recently, machine learning-based cyber attack detection and classification research has been actively conducted, achieving a high level of detection accuracy. However, low-spec IoT devices and large-scale network traffic make it difficult to apply machine learning-based detection models in IoT environment. Therefore, In this paper, we propose an efficient IoT attack detection and classification method through PCA(Principal Component Analysis) and LightGBM(Light Gradient Boosting Model) using datasets collected in a MQTT(Message Queuing Telementry Transport) IoT protocol environment that is also used in the defense field. As a result of the experiment, even though the original dataset was reduced to about 15%, the performance was almost similar to that of the original. It also showed the best performance in comparative evaluation with the four dimensional reduction techniques selected in this paper.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.51-59
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• 2024

This study examines the relationship between the release of the news and the individual stock returns. Investors utilize a variety of information sources to maximize stock returns when establishing investment strategies. News companies publish their articles based on stock recommendation reports of analysts, enhancing the reliability of the information. Defining release of a stock-recommendation news article as an event, we examine its economic impacts and propose a binary classification model that predicts the stock return 10 days after the event. XGBoost and LightGBM models are applied for the study with accuracy of 75%, 71% respectively. In addition, after categorizing the recommended stocks based on the listed market(KOSPI/KOSDAQ) and market capitalization(Big/Small), this study verifies difference in the accuracy of models across four sub-datasets. Finally, by conducting SHAP(Shapley Additive exPlanations) analysis, we identify the key variables in each model, reinforcing the interpretability of models.

• Journal of Korean Neurosurgical Society
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• v.55 no.1
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• pp.54-56
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• 2014

Intracranial tumors secondary to radiotherapy are rare. In this group gliomas are the rarest. Only 6 cases of glioblastoma multiforme (GBM) have been reported in patients undergoing radiotherapy (RT) for craniopharyngiomas of which only 4 have been in children less than 18 years of age. In recent years RT has become a mainstay of adjuvant therapy for recurrent or partially excised craniopharyngiomas. We report a child of 12 years who had previously undergone RT for a suprasellar craniopharyngioma and presented 10 years later with a GBM. This is the 5th pediatric case in literature demonstrating a GBM after RT for a craniopharyngioma. The implications of subjecting the pediatric population to RT for a benign lesion versus the outcome of gross total removal and management of RT induced tumors is discussed and the need to avail of safer alternatives such as stereotactic radiosurgery is stressed.

• BMB Reports
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• v.44 no.3
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• pp.158-164
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• 2011

Gliomas are the most frequently occurring primary malignancies in the central nervous system, and glioblastoma multiforme (GBM) is the most common and most aggressive of these tumors. Despite vigorous basic and clinical studies over past decades, the median survival of patients with this disease remains at about one year. Recent studies have suggested that GBMs contain a subpopulation of tumor cells that displays stem cell characteristics and could therefore be responsible for in vivo tumor growth. We will summarize the major oncogenic pathways abnormally regulated in gliomas, and review the recent findings from mouse models that our laboratory as well as others have developed for the study of GBM. The concept of cancer stem cells in GBM and their potential therapeutic importance will also be discussed.