• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.558-564
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• 2021

With the recent introduction of 5G, electromagnetic radiation sources are spreading throughout life, so it is necessary to establish a citizen-centered electromagnetic safety management system. In particular, the beamforming method of the 5G antenna increases the power density measurement of electromagnetic waves by more than 10 times when the wireless base station is installed, so it is unreasonable to determine the safety by physical measurement. Therefore, it is necessary to determine the presence or absence of electromagnetic wave safety in daily life through a predictive method by calculation through systematic model analysis. In this paper, in order to check the possibility of a 5G wireless base station using an electromagnetic wave numerical analysis tool as a way to solve this problem, we compared the measured values of the actual base stations and the predicted values through the prediction model to compare the reliability. A method of constructing a real-time base station electromagnetic wave strength prediction evaluation system combined with software was also proposed.

• Clinical and Experimental Reproductive Medicine
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• v.48 no.2
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• pp.163-173
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• 2021

Objective: This study aimed to characterize a validated model for predicting oocyte retrieval in controlled ovarian stimulation (COS) and to construct model-based nomograms for assistance in clinical decision-making regarding the gonadotropin protocol and dose. Methods: This observational, retrospective, cohort study included 636 women with primary unexplained infertility and a normal menstrual cycle who were attempting assisted reproductive therapy for the first time. The enrolled women were split into an index group (n=497) for model building and a validation group (n=139). The primary outcome was absolute oocyte count. The dose-response relationship was tested using modified Poisson, negative binomial, hybrid Poisson-E_max, and linear models. The validation group was similarly analyzed, and its results were compared to that of the index group. Results: The Poisson model with the log-link function demonstrated superior predictive performance and precision (Akaike information criterion, 2,704; λ=8.27; relative standard error (λ)=2.02%). The covariate analysis included women's age (p<0.001), antral follicle count (p<0.001), basal follicle-stimulating hormone level (p<0.001), gonadotropin dose (p=0.042), and protocol type (p=0.002 and p<0.001 for short and antagonist protocols, respectively). The estimates from 500 bootstrap samples were close to those of the original model. The validation group showed model assessment metrics comparable to the index model. Based on the fitted model, a static nomogram was built to improve visualization. In addition, a dynamic electronic tool was created for convenience of use. Conclusion: Based on our validated model, nomograms were constructed to help clinicians individualize the stimulation protocol and gonadotropin doses in COS cycles.

• The Korean Journal of Emergency Medical Services
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• v.26 no.2
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• pp.7-18
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• 2022

Purpose: This study aimed to develop a pre-hospital subarachnoid hemorrhage (SAH) prediction tool by analyzing the extant predictive factors of patients with non-traumatic SAH who visited the hospital through the 119 emergency medical services. Methods: We retrospectively reviewed pre-hospital care reports (PCRs) and electronic medical records (EMRs) of 103 patients with non-traumatic SAH who were transported to the emergency department of two national hospitals via the 119 emergency medical service from January 1, 2017 to December 31, 2020. Variables required to apply the Ottawa SAH Rule and EMERALD SAH Rule, which are early prediction tools for SAH, were extracted and applied. Results: The most common symptoms-which were found in 94.1% and 97.0% of all patients according to PCRs and EMRs, respectively-appeared in the following order: headache, altered state of consciousness, and nausea/vomiting. When the variables used for the EMERALD Rule, namely systolic blood pressure (SBP), diastolic blood pressure (DBP), and blood sugar test (BST), were applied, the sensitivities of EMR and PCRs were 99.9% and 92.2%, respectively. Conclusion: For the timely prediction of SAH at the pre-hospital phase, patient age and symptoms should be assessed, and SBP, DBP, and BST should be measured to transport the patient to an appropriate hospital.

• Steel and Composite Structures
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• v.48 no.2
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• pp.179-190
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• 2023

Many recent attempts have sought accurate prediction of pile pullout resistance (P_ul) using classical machine learning models. This study offers an improved methodology for this objective. Adaptive neuro-fuzzy inference system (ANFIS), as a popular predictor, is trained by a capable metaheuristic strategy, namely equilibrium optimizer (EO) to predict the P_ul. The used data is collected from laboratory investigations in previous literature. First, two optimal configurations of EO-ANFIS are selected after sensitivity analysis. They are next evaluated and compared with classical ANFIS and two neural-based models using well-accepted accuracy indicators. The results of all five models were in good agreement with laboratory P_uls (all correlations > 0.99). However, it was shown that both EO-ANFISs not only outperform neural benchmarks but also enjoy a higher accuracy compared to the classical version. Therefore, utilizing the EO is recommended for optimizing this predictive tool. Furthermore, a comparison between the selected EO-ANFISs, where one employs a larger population, revealed that the model with the population size of 75 is more efficient than 300. In this relation, root mean square error and the optimization time for the EO-ANFIS (75) were 19.6272 and 1715.8 seconds, respectively, while these values were 23.4038 and 9298.7 seconds for EO-ANFIS (300).

• Geomechanics and Engineering
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• v.35 no.5
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• pp.487-497
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• 2023

Penetration rate (PR) and penetration depth (Pe) are crucial parameters for estimating the cost and time required in tunnel construction using tunnel boring machines (TBMs). This study focuses on investigating the impact of rock strength on PR and Pe through full-scale experiments. By conducting controlled tests on rock-like specimens, the study aims to understand the contributions of various ground parameters and machine-operating conditions to TBM excavation performance. An earth pressure balanced (EPB) TBM with a sectional diameter of 3.54 m was utilized in the experiments. The TBM excavated rocklike specimens with varying uniaxial compressive strength (UCS), while the thrust and cutterhead rotational speed were controlled. The results highlight the significance of the interplay between thrust, cutterhead speed, and rock strength (UCS) in determining Pe. In high UCS conditions exceeding 70 MPa, thrust plays a vital role in enhancing Pe as hard rock requires a greater thrust force for excavation. Conversely, in medium-to-low UCS conditions less than 50 MPa, thrust has a weak relationship with Pe, and Pe becomes directly proportional to the cutterhead rotational speed. Furthermore, a strong correlation was observed between Pe and cutterhead torque with a determination coefficient of 0.84. Based on these findings, a predictive model for Pe is proposed, incorporating thrust, TBM diameter, number of disc cutters, and UCS. This model offers a practical tool for estimating Pe in different excavation scenarios. The study presents unprecedented full-scale TBM excavation results, with well-controlled experiments, shedding light on the interplay between rock strength, TBM operational variables, and excavation performance. These insights are valuable for optimizing TBM excavation in grounds with varying strengths and operational conditions.

• Journal of Korean Biological Nursing Science
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• v.26 no.1
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• pp.26-40
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• 2024

Purpose: The aim of this study was to predict the subgroups vulnerable to poorer health-related quality of life (HRQoL) according to gender in older adults. Methods: Data from 5,553 Koreans aged 65 or older were extracted from the Korea National Health and Nutrition Examination Survey. HRQoL was assessed using the EQ-5D tool. Complex sample analysis and decision-tree analysis were conducted using SPSS for Windows version 27.0. Results: The mean scores of the EQ-5D index were 0.93 ± 0.00 in men and 0.88 ± 0.00 in women. In men, poorer HRQoL groups were identified with seven different pathways, which were categorized based on participants' characteristics, such as restriction of activity, perceived health status, muscle exercise, age, relative hand grip strength, suicidal ideation, the number of chronic diseases, body mass index, and income status. Restriction of activity was the most significant predictor of poorer HRQoL in elderly men. In women, the poorer HRQoL groups were identified with nine different pathways, which were categorized based on participants' characteristics, such as perceived health status, restriction of activity, age, education, unmet medical service needs, anemia, body mass index, relative hand grip, and aerobic exercise. Perceived health status was the most significant predictor of poorer HRQoL in elderly women. Conclusion: This study presents a predictive model of HRQoL in older adults according to gender and can be used to detect individuals at risk of poorer HRQoL.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.65-72
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• 2024

Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early-stage decision-making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning-based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting-edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.27 no.2
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• pp.195-200
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• 2023

Backgrounds/Aims: We aimed to build a machine learning tool to help predict low-grade intraductal papillary mucinous neoplasms (IPMNs) in order to avoid unnecessary surgical resection. IPMNs are precursors to pancreatic cancer. Surgical resection remains the only recognized treatment for IPMNs yet carries some risks of morbidity and potential mortality. Existing clinical guidelines are imperfect in distinguishing low-risk cysts from high-risk cysts that warrant resection. Methods: We built a linear support vector machine (SVM) learning model using a prospectively maintained surgical database of patients with resected IPMNs. Input variables included 18 demographic, clinical, and imaging characteristics. The outcome variable was the presence of low-grade or high-grade IPMN based on post-operative pathology results. Data were divided into a training/validation set and a testing set at a ratio of 4:1. Receiver operating characteristics analysis was used to assess classification performance. Results: A total of 575 patients with resected IPMNs were identified. Of them, 53.4% had low-grade disease on final pathology. After classifier training and testing, a linear SVM-based model (IPMN-LEARN) was applied on the validation set. It achieved an accuracy of 77.4%, with a positive predictive value of 83%, a specificity of 72%, and a sensitivity of 83% in predicting low-grade disease in patients with IPMN. The model predicted low-grade lesions with an area under the curve of 0.82. Conclusions: A linear SVM learning model can identify low-grade IPMNs with good sensitivity and specificity. It may be used as a complement to existing guidelines to identify patients who could avoid unnecessary surgical resection.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.551-571
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• 2023

Purpose: The purpose of this study is to propose useful suggestions by analyzing the causal effect relationship between the failure rate of quality and the process variables in the C5ISR domain of the defense industry. Methods: The collected data through the in house Systems were analyzed using Big data analysis. Data analysis between quality data and A/S history data was conducted using the CRISP-DM(Cross-Industry Standard Process for Data Mining) analysis process. Results: The results of this study are as follows: After evaluating the performance of candidate models for the influence of inspection data and A/S history data, logistic regression was selected as the final model because it performed relatively well compared to the decision tree with an accuracy of 82%/67% and an AUC of 0.66/0.57. Based on this model, we estimated the coefficients using 'R', a data analysis tool, and found that a specific variable(continuous maximum discharge current time) had a statistically significant effect on the A/S quality failure rate and it was analysed that 82% of the failure rate could be predicted. Conclusion: As the first case of applying big data analysis to quality issues in the defense industry, this study confirms that it is possible to improve the market failure rates of defense products by focusing on the measured values of the main causes of failures derived through the big data analysis process, and identifies improvements, such as the number of data samples and data collection limitations, to be addressed in subsequent studies for a more reliable analysis model.

• Journal of the Korean Society of Radiology
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• v.85 no.2
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• pp.258-269
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• 2024

Treatment of patients with coronary artery disease commonly involves the use of balloon-expandable stent placements, currently recognized as the most prevalent approach for coronary artery revascularization. Nevertheless, the occurrence of restenosis remains a significant complication following percutaneous coronary interventions. The diagnostic role of coronary CT angiography (CCTA) in detecting stent restenosis has limitations primarily attributable to challenges in accurately discerning the lumen, due to issues such as blooming and motion artifacts. As a result, many cases often necessitate a transition to conventional coronary angiography. However, recent advancements in CT technology have led to notable improvements in both sensitivity and specificity, underscoring the growing significance of CCTA as a diagnostic tool. The consistent reporting of high negative predictive value is particularly noteworthy. This review aims to explore the historical context, current status, and recent trends in diagnosing coronary artery stent restenosis using CCTA.