• Journal of Yeungnam Medical Science
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• v.6 no.1
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• pp.1-7
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• 1989

• Nuclear Engineering and Technology
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• v.38 no.3
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• pp.231-238
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• 2006

Whole-body exposure to high-dose radiation causes injury involving multiple organs that depends on their sensitivity to radiation. This acute radiation syndrome (ARS) is caused by a brief exposure of a major part of the body to radiation at a relatively high dose rate. ARS is characterized by an initial prodromal stage, a latent symptom-free period, a critical or manifestation phase that usually takes one of four forms (three forms): hematologic, gastrointestinal, or cardiovascular and neurological (neurovascular), depending upon the exposure dose, and a recovery phase or death. One of the most important factors in treating victims exposed to radiation is the estimation of the exposure dose. When high-dose exposure is considered, initial dose estimation must be performed in order to make strategy decisions for treatment as soon as possible. Dose estimation can be based on onset and severity of prodromal symptoms, decline in absolute lymphocyte count post exposure, and chromosomal analysis of peripheral blood lymphocytes. Moreover, dose assessment on the basis of calculation from reconstruction of the radiation event may be required. Experience of a criticality accident occurring in 1999 at Tokai-mura, Japan, showed that ARS led to multiple organ failure (MOF). This article will review ARS and discuss the possible mechanisms of MOF developing from ARS.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.290-300
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• 2024

This study presents a comprehensive evaluation of various machine learning models for predicting heart failure outcomes. Leveraging a data set of clinical records, the performance of Logistic Regression, Support Vector Machine (SVM), Random Forest, Soft Voting ensemble, and XGBoost models are rigorously assessed using multiple evaluation metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The analysis reveals that the XGBoost model outperforms the other techniques across all metrics, exhibiting the highest AUC score, indicating superior discriminative ability in distinguishing between patients with and without heart failure. Furthermore, the study highlights the importance of feature importance analysis provided by XGBoost, offering valuable insights into the most influential predictors of heart failure, which can inform clinical decision-making and patient management strategies. The research also underscores the significance of balancing precision and recall, as reflected by the F1-score, in medical applications to minimize the consequences of false negatives.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.18-24
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• 2020

This research conducted an the failure analysis was performed based on the failure and operation data for Seven years using the Reliability, Availability, Maintainability, and Safety(RAMS) constructed at the operation stage after the opening of the D urban railway. therefore, the risk priority was selected for failure frequency component within the door system that showed high failure. Finally, the goal was to suggest ways to improve the door system. For this purpose, the analysis of thermal characteristics of failed components such as Door Control Unit(DCU) in the door system based on the Seven-year failure analysis data of RAMS was performed. These results were applied to the main component exchange cycle of the door unit, the mean time between failure(MTBF) and mean kilometer between failure(MKBF) values of RAMS increased by 26% in 2017-2018 when the improvement measures were taken, and the MTBF value of DCU was 300,000 hours, which was a 57% improvement in reliability. The results of this thesis identify potential enhancements in reliability and improvements in maintenance of the door system that, if implemented, would contribute to train safety and reduce instances of failure in the future.

• Journal of the Korean Institute of Gas
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• v.22 no.6
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• pp.40-43
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• 2018

For pressure safety valves, open failure and close failure are partially dependent on each other. A method is proposed in this work that uses a Markov process model and a Weibull distribution model in order to construct a reliability model for two kinds of failure. A pressure safety valve model is obtained from a known open failure model, an induced close failure model, and a simultaneous failure model that reproduces recently reported inspection results. It is expected that the application of the proposed method can be expanded to quantitative risk assessment of various systems that have partially dependent multiple failure states.

• Geomechanics and Engineering
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• v.12 no.4
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• pp.657-673
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• 2017

Preventing water seepage and inrush into mines where close multiple-seam longwall mining is practiced is a challenging issue in the coal-rich Ordos region, China. To better protect surface (or ground) water and safely extract coal from seams beneath an aquifer, it is necessary to determine the height of the mining-induced fractured zone in the overburden strata. In situ investigations were carried out in panels 20107 (seam No. $2-2^{upper}$) and 20307 (seam No. $2-2^{middle}$) in the Gaojialiang colliery, Shendong Coalfield, China. Longwall mining-induced strata movement and overburden failure were monitored in boreholes using digital panoramic imaging and a deep hole multi-position extensometer. Our results indicate that after mining of the 20107 working face, the overburden of the failure zone can be divided into seven rock groups. The first group lies above the immediate roof (12.9 m above the top of the coal seam), and falls into the gob after the mining. The strata of the second group to the fifth group form the fractured zone (12.9-102.04 m above the coal seam) and the continuous deformation zone extends from the fifth group to the ground surface. After mining Panel 20307, a gap forms between the fifth rock group and the continuous deformation zone, widening rapidly. Then, the lower portion of the continuous deformation zone cracks and collapses into the fractured zone, extending the height of the failure zone to 87.1 m. Based on field data, a statistical formula for predicting the maximum height of overburden failure induced by close multiple seam mining is presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3086-3101
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• 2021

To supply precise marketing and differentiated service for the electric power service department, it is very important to predict the customers with high sensitivity of electric power failure. To solve this problem, we propose a novel grouped 𝑙_1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the 𝑙₁ norm and k-support norm, the proposed grouped 𝑙_1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex 𝑙₀ sparsity to exploit multiple correlated attributions for prediction. Firstly, the attributes or factors for predicting the customer sensitivity of power failure are selected from customer sheets, such as customer information, electric consuming information, electrical bill, 95598 work sheet, power failure events, etc. Secondly, all these samples with attributes are clustered into several categories, and samples in the same category are assumed to be sharing similar properties. Then, 𝑙_1/2 norm constrained logistic regression model is built to predict the customer's sensitivity of power failure. Alternating direction of multipliers (ADMM) algorithm is finally employed to solve the problem by splitting it into several sub-problems effectively. Experimental results on power electrical dataset with about one million customer data from a province validate that the proposed method has a good prediction accuracy.

• International Journal of Heart Failure
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• v.6 no.1
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• pp.11-19
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• 2024

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

• Clinical and Experimental Reproductive Medicine
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• v.44 no.2
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• pp.105-110
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• 2017

Objective: The purpose of this study was to evaluate the efficacy of frozen mixed double-embryo transfer (MDET; the simultaneous transfer of day 3 and day 5 embryos) in comparison with frozen blastocyst double-embryo transfer (BDET; transfer of two day 5 blastocysts) in patients with repeated implantation failure (RIF). Methods: A total of 104 women with RIF who underwent frozen MDET (n = 48) or BDET (n = 56) with excellent-quality embryos were included in this retrospective analysis. All frozen embryo transfers were performed in natural cycles. The main outcome measures were the implantation rate, clinical pregnancy rate, multiple pregnancy rate, and miscarriage rate. These measures were compared between the patients who underwent MDET or BDET using the chi-square test or the Fisher exact test, as appropriate. Results: The implantation and clinical pregnancy rates were significantly higher in patients who underwent MDET than in those who underwent BDET (60.4% vs. 39.3%, p=0.03 and 52.1% vs. 30.4%, p=0.05, respectively). A significantly lower miscarriage rate was observed in the MDET group (6.9% vs. 10.7%, p=0.05). In addition, the multiple pregnancy rate was slightly, but not significantly, higher in the MDET group (27.1% vs. 25.0%). Conclusion: MDET was found to be significantly superior to double blastocyst transfer. It could be regarded as an appropriate approach to improve in vitro fertilization success rates in RIF patients.

• Computers and Concrete
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• v.13 no.2
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• pp.255-270
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• 2014

Impact performance of high-performance concrete (HPC) and SFRC at 28-day and 56-day under the action of repeated dynamic loading was studied. Silica fume replacement at 10% and 15% by mass and crimped steel fiber ($V_f$ = 0.5%- 1.5%) with aspect ratios of 80 and 53 were used in the concrete mixes. Results indicated that addition of fibers in HPC can effectively restrain the initiation and propagation of cracks under stress, and enhance the impact strengths and toughness of HPC. Variation of fiber aspect ratio has minor effect on improvement in impact strength. Based on the experimental data, failure resistance prediction models were developed with correlation coefficient (R) = 0.96 and the estimated absolute variation is 1.82% and on validation, the integral absolute error (IAE) determined is 10.49%. On analyzing the data collected, linear relationship for the prediction of failure resistance with R= 0.99 was obtained. IAE value of 10.26% for the model indicates better the reliability of model. Multiple linear regression model was developed to predict the ultimate failure resistance with multiple R= 0.96 and absolute variation obtained is 4.9%.