• Journal of Korean Port Research
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• v.14 no.4
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• pp.419-427
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• 2000

Today travel demand continues to increase with spread of economic zones. Also, urban freeway plays an important role in intra-zone transportations as a major corridor in a big city. However, most of urban freeways experience a severe congestion with the excess of inflowing or outflowing traffic through freeway ramps. The purpose of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the speed predictive models on the ramp junctions of urban freeway under the intelligent transportation system(ITS) settings. From the analyses of traffic characteristics following results were obtained: ⅰ) 24 hours average traffic characteristics flow, occupancy, speed under the ITS settings showed about 40%, 38%, 8.8% increase each on urban freeway junctions period when compared with that under the non-ITS settings each other. Free flow speed and traffic flow on the mainline sections of urban freeway under the ITS settings also showed about 20% and 17% increase when compared with that under the non-ITS, respectively. ⅱ) The upstream when compared speed( $S_{u}$)and downstream occupancy( $O_{d}$) were especially shown to have higher explanatory powers on the stable flow ramp junctions, but the upstream speed( $S_{u}$) and downstream flow( $V_{d}$) were especially shown on the unstable flow ramp junctions of urban freeway under the ITS settings.ngs.ngs.

• Annals of Clinical Neurophysiology
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• v.9 no.2
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• pp.63-68
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• 2007

The EEG plays an important diagnostic role in epilepsy and provides supporting evidence of a seizure disorder as well as assisting with classification of seizures and epilepsy syndromes. There are a variety of electroclinical syndromes that are really defined by the EEG such as Lennox-Gastaut syndrome, benign rolandic epilepsy, childhood absence epilepsy, juvenile myoclonic epilepsy and also for localization purposes, it is vitally important especially for temporal lobe epilepsy. The sensitivity of first routine EEG in diagnosis of epilepsy has been known about 20-50%, but this proportion rises to 80-90% if sleep EEG and repetitive recording should be added. Convincing evidences suggest that the EEG may also provide useful prognostic information regarding seizure recurrence after a single unprovoked attack and following antiepileptic drug (AED) withdrawal. Moreover, patterns in the EEG make it possible to disclose an ictal feature of nonconvulsive status epilepticus, separate epileptic from other non-epileptic episodes and clarify the clues predictive of the cause of the encephalopathy (i.e., triphasic waves in metabolic encephalopathy). Therefore, regardless of its low sensitivity and other pitfalls, EEG should be considered not only in the situation of new onset episode such as a newly developed, unprovoked seizure or a condition manifesting decreased mentality from obscure origin, but also as a barometer of the long-term outcome following AED withdrawal.

• Safety and Health at Work
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• v.12 no.1
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• pp.1-9
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• 2021

This review article addresses the role of safety professionals in the diffusion strategies for predictive analytics for safety performance. The article explores the models, definitions, roles, and relationships of safety professionals in knowledge application, access, management, and leadership in safety analytics. The article addresses challenges safety professionals face when integrating safety analytics in organizational settings in four operations areas: application, technology, management, and strategy. A review of existing conventional safety data sources (safety data, internal data, external data, and context data) is briefly summarized as a baseline. For each of these data sources, the article points out how emerging analytic data sources (such as Industry 4.0 and the Internet of Things) broaden and challenge the scope of work and operational roles throughout an organization. In doing so, the article defines four perspectives on the integration of predictive analytics into organizational safety practice: the programmatic perspective, the technological perspective, the sociocultural perspective, and knowledge-organization perspective. The article posits a four-level, organizational knowledge-skills-abilities matrix for analytics integration, indicating key organizational capacities needed for each area. The work shows the benefits of organizational alignment, clear stakeholder categorization, and the ability to predict future safety performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.205-205
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• 2021

Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

• Journal of Korea Artificial Intelligence Association
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• v.2 no.1
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• pp.7-14
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• 2024

In this paper, we explore the application of RNA-sequencing data and ensemble machine learning to predict lung cancer and treatment strategies for lung cancer, a leading cause of cancer mortality worldwide. The research utilizes Random Forest, XGBoost, and LightGBM models to analyze gene expression profiles from extensive datasets, aiming to enhance predictive accuracy for lung cancer prognosis. The methodology focuses on preprocessing RNA-seq data to standardize expression levels across samples and applying ensemble algorithms to maximize prediction stability and reduce model overfitting. Key findings indicate that ensemble models, especially XGBoost, substantially outperform traditional predictive models. Significant genetic markers such as ADGRF5 is identified as crucial for predicting lung cancer outcomes. In conclusion, ensemble learning using RNA-seq data proves highly effective in predicting lung cancer, suggesting a potential shift towards more precise and personalized treatment approaches. The results advocate for further integration of molecular and clinical data to refine diagnostic models and improve clinical outcomes, underscoring the critical role of advanced molecular diagnostics in enhancing patient survival rates and quality of life. This study lays the groundwork for future research in the application of RNA-sequencing data and ensemble machine learning techniques in clinical settings.

• Journal of Gastric Cancer
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• v.24 no.1
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• pp.4-28
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• 2024

Liquid biopsy, a minimally invasive procedure that causes minimal pain and complication risks to patients, has been extensively studied for cancer diagnosis and treatment. Moreover, it facilitates comprehensive quantification and serial assessment of the whole-body tumor burden. Several biosources obtained through liquid biopsy have been studied as important biomarkers for establishing early diagnosis, monitoring minimal residual disease, and predicting the prognosis and response to treatment in patients with cancer. Although the clinical application of liquid biopsy in gastric cancer is not as robust as that in other cancers, biomarker studies using liquid biopsy are being actively conducted in patients with gastric cancer. Herein, we aimed to review the role of various biosources that can be obtained from patients with gastric cancer through liquid biopsies, such as blood, saliva, gastric juice, urine, stool, peritoneal lavage fluid, and ascites, by dividing them into cellular and acellular components. In addition, we reviewed previous studies on the diagnostic, prognostic, and predictive biomarkers for gastric cancer using liquid biopsy and discussed the limitations of liquid biopsy and the challenges to overcome these limitations in patients with gastric cancer.

• Women's Health Nursing
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• v.9 no.1
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• pp.50-60
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• 2003

Purpose : The purpose of this study was to evaluate the factors which are related to the maternal role performance of first-time mother to improve the health of infant. Specifically a basic hypothetical model was developed based on the previous study about a model of social networks. Method : The survey was done from January to February in 2001. Total 257 mothers who have four to twelve month old first-time baby was interviewed in five community health center around country(Seoul, Choung-ju, Asan, Cheon-an, Jeju). Finally 247 data was analyzed. Data analysis was done with LISREL 8.20 program for covariance structural analysis. Results : Compared to the hypothetical model, the revised model has become parsimonious and had a better fit to the data ($X^2=167.55$ (p값=.00), $x^2/df=1.48$, GFI=0.97, AGFI=0.95, RMR=0.049, NFI=0.98, NNFI=0.99, CN=222.53). All predictive variables of the maternal role of first-time mother explained 30% of total variance in model. Social network structural characteristics and social network interactional characteristics had significant effect on the emotional support and the information support. And social network interactional characteristics had significant effect on the service support, material support and social companionship support. The service support and social companion ship support had significant effect on the maternal role strain. The emotional support and the social companion ship support had significant effect on the maternal role of first-time mother. Conclusion : As the conclusion of this study, there is in need of the developing the programmes focussed on the social network for the first-time mother.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6121-6128
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• 2012

Radiation-induced side effects on normal tissue are determined largely by the capacity of cells to repair radiation-induced DNA damage. X-ray repair cross-complementing group 1 (XRCC1) plays an important role in the repair of DNA single-strand breaks. Studies have shown conflicting results regarding the association between XRCC1 gene polymorphisms (Arg399Gln, Arg194Trp, -77T>C and Arg280His) and radiation-induced side effects in patients undergoing whole breast radiotherapy. Therefore, we conducted a meta-analysis to determine the predictive value of XRCC1 gene polymorphisms in this regard. Analysis of the 11 eligible studies comprising 2,199 cases showed that carriers of the XRCC1 399 Gln allele had a higher risk of radiation-induced toxicity than those with the 399 ArgArg genotype in studies based on high-quality genotyping methods [Gln vs. ArgArg: OR, 1.85; 95% CI, 1.20-2.86] or in studies with mixed treatment regimens of radiotherapy alone and in combination with chemotherapy [Gln vs. ArgArg: OR, 1.60; 95% CI, 1.09-2.23]. The XRCC1 Arg399Gln variant allele was associated with mixed acute and late adverse reactions when studies on late toxicity only were excluded [Gln allele vs. Arg allele: OR, 1.22; 95% CI, 1.00-1.49]. In contrast, the XRCC1 Arg280His variant allele was protective against radiation-induced toxicity in studies including patients treated by radiotherapy alone [His allele vs. Arg allele: OR, 0.58; 95% CI, 0.35-0.96]. Our results suggest that XRCC1 399Gln and XRCC1 280Arg may be independent predictors of radiation-induced toxicity in post-surgical breast cancer patients, and the selection of genotyping method is an important factor in determining risk factors. No evidence for any predictive value of XRCC1 Arg194Trp and XRCC1 -77T>C was found. So, larger and well-designed studies might be required to further evaluate the predictive value of XRCC1 gene variation on radiation-induced side effects in patients undergoing whole breast radiotherapy.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.13-21
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• 2016

Insolvency of construction companies that play the role of main contractors can lead to clients' losses due to non-fulfillment of construction contracts, and it can have negative effects on the financial soundness of construction companies and suppliers. The construction industry has the cash flow financial characteristic of receiving a project and getting payment based on the progress of the construction. As such, insolvency during project progress can lead to financial losses, which is why the prediction of construction companies is so important. The prediction of insolvency of Korean construction companies are often made through the KMV model from the KMV (Kealhofer McQuown and Vasicek) Company developed in the U.S. during the early 90s, but this model is insufficient in predicting construction companies because it was developed based on credit risk assessment of general companies and banks. In addition, the predictive performance of KMV value's insolvency probability is continuously being questioned due to lack of number of analyzed companies and data. Therefore, in order to resolve such issues, the Bayesian Probabilistic Approach is to be combined with the existing insolvency predictive probability model. This is because if the Prior Probability of Bayesian statistics can be appropriately predicted, reliable Posterior Probability can be predicted through ensured conditionality on the evidence despite the lack of data. Thus, this study is to measure the Expected Default Frequency (EDF) by utilizing the Bayesian Probabilistic Approach with the existing insolvency predictive probability model and predict the accuracy by comparing the result with the EDF of the existing model.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.8
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• pp.3733-3736
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• 2016

HER2/neu is a well-established prognostic and predictive factor for invasive breast cancer. However, the role of HER2/neu in ductal breast carcinoma in situ (DCIS) is debated and recent data have suggested that it is mainly linked to in situ local recurrence. Although molecular data suggest that atypical ductal hyperplasia (ADH) and duct carcinoma in situ (DCIS) are related lesions, albeit with vastly different clinical implications, the role of HER2/neu expression in atypical ductal hyperplasia is not well defined either. The aim of this study was to evaluate over expression of HER2/neu in DCIS and cases of ADH in comparison with invasive breast carcinoma. Archival primary breast carcinoma paraffin blocks (n=15), DCIS only (n=10) and ductal epithelial hyperplasia and other breast benign lesions (n=25) were analyzed for HER2/neu immunoexpression. Follow up was available for 40% of the patients. HER2/neu was positive in 80%of both DCIS and invasive carcinoma, and 67% of atypical ductal hyperplasia (ADH) cases. Thus at least a subset of patients with preinvasive breast lesions were positive, which strongly suggests a role for Her2/neu in identifying high-risk patients for malignant transformation. Although these are preliminary data, which need further studies of gene amplification within these patients as well as a larger patient cohort with longer periods of follow up, they support the implementation of routine Her2/neu testing in patients diagnosed as pure DCIS and in florid ADH.