• Molecules and Cells
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• v.42 no.2
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• pp.161-165
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• 2019

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths worldwide and has high rates of metastasis. Transforming growth factor beta-inducible protein (TGFBI) is an extracellular matrix component involved in tumour growth and metastasis. However, the exact role of TGFBI in NSCLC remains controversial. Gene silencing via DNA methylation of the promoter region is common in lung tumorigenesis and could thus be used for the development of molecular biomarkers. We analysed the methylation status of the TGFBI promoter in 138 NSCLC specimens via methylation-specific PCR and evaluated the correlation between TGFBI methylation and patient survival. TGFBI promoter methylation was detected in 25 (18.1%) of the tumours and was demonstrated to be associated with gene silencing. We observed no statistical correlation between TGFBI methylation and clinicopathological characteristics. Univariate and multivariate analyses showed that TGFBI methylation is significantly associated with poor survival outcomes in adenocarcinoma cases (adjusted hazard ratio = 2.88, 95% confidence interval = 1.19-6.99, P = 0.019), but not in squamous cell cases. Our findings suggest that methylation in the TGFBI promoter may be associated with pathogenesis of NSCLC and can be used as a predictive marker for lung adenocarcinoma prognosis. Further large-scale studies are needed to confirm these findings.

• Steel and Composite Structures
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• v.39 no.4
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• pp.471-491
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• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.

• Structural Engineering and Mechanics
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• v.76 no.5
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• pp.561-578
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• 2020

The present work addresses the surface integrity and chip morphology in finish hard turning of AISI D3 steel under nanofluid assisted minimum quantity lubrication (NFMQL) condition. The surface integrity aspects include microhardness, residual stress, white layer formation, machined surface morphology, and surface roughness. This experimental investigation aims to explore the feasibility of low-cost multilayer (TiCN/Al2O3/TiN) coated carbide tool in hard machining applications and to assess the propitious role of minimum quantity lubrication using graphene nanoparticles enriched eco-friendly radiator coolant based nano-cutting fluid for machinability improvement of hardened steel. Combined approach of central composite design (CCD) - analysis of variance (ANOVA), desirability function analysis, and response surface methodology (RSM) have been subsequently employed for experimental investigation, predictive modelling and optimization of surface roughness. With a motivational philosophy of "Go Green-Think Green-Act Green", the work also deals with economic analysis, and sustainability assessment under environmental-friendly NFMQL condition. Results showed that machining with nanofluid-MQL provided an effective cooling-lubrication strategy, safer and cleaner production, environmental friendliness and assisted to improve sustainability.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.2
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• pp.36-46
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• 2021

The purpose of this study is to ensure safety by proactively identifying hazards that could be derived from changes in mission form and environment as the advanced aircraft such as F-35A stealth fighter, KC-330 Multi-role transport and tanker, RQ-4B high altitude unmanned reconnaissance aircraft, etc are introduced that the Republic of Korea Air Force(ROKAF) has never been operated so far. To this end, the safety management methods based on proactive and predictive approaches used in advanced countries(US Air Force, UK Royal Air Force, Royal Australian Air Force) operating aircraft types same or similar things being newly powered by the ROKAF were reviewed. In addition, the direction for improvement of the safety management methods operating in the ROKAF and the measures necessary for establishment of the new safety management techniques to be applied were suggested.

• Korean Journal of Artificial Intelligence
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• v.11 no.1
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• pp.1-7
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• 2023

In this paper, a study was conducted to compare the prediction model of cardiovascular disease occurrence. It is the No.1 disease that accounts for 1/3 of the world's causes of death, and it is also the No. 2 cause of death in Korea. Primary prevention is the most important factor in preventing cardiovascular diseases before they occur. Early diagnosis and treatment are also more important, as they play a role in reducing mortality and morbidity. The Results of an experiment using Azure ML, Logistic Regression showed 88.6% accuracy, Decision Tree showed 86.4% accuracy, and Support Vector Machine (SVM) showed 83.7% accuracy. In addition to the accuracy of the ROC curve, AUC is 94.5%, 93%, and 92.4%, indicating that the performance of the machine learning algorithm model is suitable, and among them, the results of applying the logistic regression algorithm model are the most accurate. Through this paper, visualization by comparing the algorithms can serve as an objective assistant for diagnosis and guide the direction of diagnosis made by doctors in the actual medical field.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.123-140
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• 2023

In recent times, the global economy has been subject to increasing volatility, which has made it considerably more difficult to accurately predict economic indicators compared to previous periods. In response to this challenge, the present study conducts an exploratory investigation that aims to predict the Business Survey Index (BSI) by leveraging data mining techniques on both structured and unstructured data sources. For the structured data, we have collected information regarding foreign, domestic, and industrial conditions, while the unstructured data consists of content extracted from newspaper articles. By employing an extensive set of 44 distinct data mining techniques, our research strives to enhance the BSI prediction accuracy and provide valuable insights. The results of our analysis demonstrate that the highest predictive power was attained when using data exclusively from the t-1 period. Interestingly, this suggests that previous timeframes play a vital role in forecasting the BSI effectively. The findings of this study hold significant implications for economic decision-makers, as they will not only facilitate better-informed decisions but also serve as a robust foundation for predicting a wide range of other economic indicators. By improving the prediction of crucial economic metrics, this study ultimately aims to contribute to the overall efficacy of economic policy-making and decision processes.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4460-4473
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• 2022

The behaviour of the fission gas plays an important role in the fuel rod performance. In a previous work, we presented a physics-based model describing intra- and inter-granular behaviour of radioactive fission gas. The model was implemented in SCIANTIX, a mesoscale module for fission gas behaviour, and assessed against the CONTACT 1 irradiation experiment. In this work, we present the multi-scale coupling between the TRANSURANUS fuel performance code and SCIANTIX, used as mechanistic module for stable and radioactive fission gas behaviour. We exploit the coupled code version to reproduce two integral irradiation experiments involving standard fuel rod segments in steady-state operation (CONTACT 1) and during successive power transients (HATAC C2). The simulation results demonstrate the predictive capabilities of the code coupling and contribute to the integral validation of the models implemented in SCIANTIX.

• The Korean Journal of Pain
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• v.36 no.2
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• pp.230-241
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• 2023

Background: This study aimed to determine the prevalence of chronic pain and its contributing factors among teenagers aged 12-21 years in Shiraz, Iran. Methods: This cross-sectional study was conducted on adolescents aged 12-21 years. Demographic variables of the adolescents and their parents as well as the pain characteristics were assessed. Descriptive statistics, multinomial logistic regression, and regression models were used to describe the characteristics of the pain and its predictive factors. Results: The prevalence of chronic pain was 23.7%. The results revealed no significant difference between the male and female participants regarding the pain characteristics, except for the home medications used for pain relief. The results of a chi-square test showed that the mother's pain, education, and occupation, and the father's education were associated significantly with chronic pain in adolescents (P < 0.05). Multinomial logistic regression also showed the mother's history of pain played a significant role in the incidence of adolescents' chronic pain. Conclusions: The prevalence of chronic pain was relatively high in these adolescents. The results also provided basic and essential information about the contributing factors in this area. However, consideration of factors such as anxiety, depression, school problems, sleep, and physical activity are suggested in future longitudinal studies.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.3
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• pp.42-49
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• 2023

Not only in aviation industry but also in other industries, safety data plays a key role to improve the level of safety performance. By analyzing safety data such as aviation safety report (text data), hazard can be identified and removed before it leads to a tragic accident. However, pre-processing of raw data (or natural language data) collected from each site should be carried out first to utilize proactive or predictive safety management system. As air traffic volume increases, the amount of data accumulated is also on the rise. Accordingly, there are clear limitation in analyzing data directly by manpower. In this paper, a topic prediction model for aviation safety mandatory report is proposed. In addition, the prediction accuracy of the proposed model was also verified using actual aviation safety mandatory report data. This research model is meaningful in that it not only effectively supports the current aviation safety mandatory report analysis work, but also can be applied to various data produced in the aviation safety field in the future.

• Korean Journal of Radiology
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• v.25 no.1
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• pp.86-102
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• 2024

Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.