• Geomechanics and Engineering
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• v.30 no.1
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• pp.11-26
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• 2022

One of the most important issues in tunneling, is the squeezing phenomenon. Squeezing can occur during excavation or after the construction of tunnels, which in both cases could lead to significant damages. Therefore, it is important to predict the squeezing and consider it in the early design stage of tunnel construction. Different empirical, semi-empirical and theoretical-analytical methods have been presented to determine the squeezing. Therefore, it is necessary to examine the ability of each of these methods and identify the best method among them. In this study, squeezing in a part of the Alborz service tunnel in Iran was estimated through a number of empirical, semi- empirical and theoretical-analytical methods. Among these methods, the most robust model was used to obtain a database including 300 data for training and 33 data for testing in order to develop a machine learning (ML) method. To this end, three ML models of Gaussian process regression (GPR), artificial neural network (ANN) and support vector regression (SVR) were trained and tested to propose a robust model to predict the squeezing phenomenon. A comparative analysis between the conventional and the ML methods utilized in this study showed that, the GPR model is the most robust model in the prediction of squeezing phenomenon. The sensitivity analysis of the input parameters using the mutual information test (MIT) method showed that, the most sensitive parameter on the squeezing phenomenon is the tangential strain (ε_θ^α) parameter with a sensitivity score of 2.18. Finally, the GPR model was recommended to predict the squeezing phenomenon in tunneling projects. This work's significance is that it can provide a good estimation of the squeezing phenomenon in tunneling projects, based on which geotechnical engineers can take the necessary actions to deal with it in the pre-construction designs.

• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.234-244
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• 2023

Background: Effective treatment of fluoroquinolone-resistant multidrug-resistant tuberculosis (FQr-MDR-TB) is difficult because of the limited number of available core anti-TB drugs and high rates of resistance to anti-TB drugs other than FQs. However, few studies have examined anti-TB drugs that are effective in treating patients with FQr-MDR-TB in a real-world setting. Methods: The impact of anti-TB drug use on treatment outcomes in patients with pulmonary FQr-MDR-TB was retrospectively evaluated using a nationwide integrated TB database (Korean Tuberculosis and Post-Tuberculosis). Data from 2011 to 2017 were included. Results: The study population consisted of 1,082 patients with FQr-MDR-TB. The overall treatment outcomes were as follows: treatment success (69.7%), death (13.7%), lost to follow-up or not evaluated (12.8%), and treatment failure (3.9%). On a propensity-score-matched multivariate logistic regression analysis, the use of bedaquiline (BDQ), linezolid (LZD), levofloxacin (LFX), cycloserine (CS), ethambutol (EMB), pyrazinamide, kanamycin (KM), prothionamide (PTO), and para-aminosalicylic acid against susceptible strains increased the treatment success rate (vs. unfavorable outcomes). The use of LFX, CS, EMB, and PTO against susceptible strains decreased the mortality (vs. treatment success). Conclusion: A therapeutic regimen guided by drug-susceptibility testing can improve the treatment of patients with pulmonary FQr-MDR-TB. In addition to core anti-TB drugs, such as BDQ and LZD, treatment of susceptible strains with later-generation FQs and KM may be beneficial for FQr-MDR-TB patients with limited treatment options.

• Computers and Concrete
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• v.31 no.2
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• pp.111-121
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• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.389-396
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• 2024

Generative AI has recently been utilized across all fields, achieving expert-level advancements in deep data analysis. However, identifying regional names in scientific literature remains a challenge due to insufficient training data and limited AI application. This study developed a standardized dataset for effectively classifying regional names using address data from Korean institution-affiliated authors listed in the Web of Science. It tested and evaluated the applicability of machine learning and deep learning models in real-world problems. The BERT model showed superior performance, with a precision of 98.41%, recall of 98.2%, and F1 score of 98.31% for metropolitan areas, and a precision of 91.79%, recall of 88.32%, and F1 score of 89.54% for city classifications. These findings offer a valuable data foundation for future research on regional R&D status, researcher mobility, collaboration status, and so on.

• Genomics & Informatics
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• v.21 no.4
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• pp.48.1-48.8
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• 2023

Liver cancer is the fourth leading cause of death worldwide. Well-known risk factors include hepatitis B virus and hepatitis C virus, along with exposure to aflatoxins, excessive alcohol consumption, obesity, and type 2 diabetes. Genomic variants play a crucial role in mediating the associations between these risk factors and liver cancer. However, the specific variants involved in this process remain under-explored. This study utilized a bioinformatics approach to identify genetic variants associated with liver cancer from various continents. Single-nucleotide polymorphisms associated with liver cancer were retrieved from the genome-wide association studies catalog. Prioritization was then performed using functional annotation with HaploReg v4.1 and the Ensembl database. The prevalence and allele frequencies of each variant were evaluated using Pearson correlation coefficients. Two variants, rs2294915 and rs2896019, encoded by the PNPLA3 gene, were found to be highly expressed in the liver tissue, as well as in the skin, cell-cultured fibroblasts, and adipose-subcutaneous tissue, all of which contribute to the risk of liver cancer. We further found that these two SNPs (rs2294915 and rs2896019) were positively correlated with the prevalence rate. Positive associations with the prevalence rate were more frequent in East Asian and African populations. We highlight the utility of this population-specific PNPLA3 genetic variant for genetic association studies and for the early prognosis and treatment of liver cancer. This study highlights the potential of integrating genomic databases with bioinformatic analysis to identify genetic variations involved in the pathogenesis of liver cancer. The genetic variants investigated in this study are likely to predispose to liver cancer and could affect its progression and aggressiveness. We recommend future research prioritizing the validation of these variations in clinical settings.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.1
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• pp.183-190
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• 2024

Purpose : This study aims to conduct a comprehensive comparison and analysis of intervention strategies utilized for school-aged children facing difficulties in writing, focusing on evaluating the effectiveness of various intervention approaches both domestically and internationally. The primary focus is on assessing the efficacy of each intervention approach and identifying gaps in the existing literature. Methods : Data for this study were gathered from the domestic database RISS from January 2013 to March 2020, and international databases Pubmed and Google Scholar were utilized. The keywords for domestic literature search included 'occupational therapy', 'handwriting', and 'school-aged', while for international literature search, the keywords were 'occupational therapy', 'handwriting', and 'children'. A total of 4 international and 2 domestic articles were selected for review based on predetermined inclusion and exclusion criteria. Results : The study findings present a thorough comparative analysis of intervention strategies, categorizing them into task-oriented intervention, sensory-motor intervention, and integrated intervention. All intervention methods demonstrated notable improvements in the legibility of handwriting. Comparison between domestic and international literature revealed a predominant use of task-oriented intervention in domestic studies, while international studies showcased a diverse range of intervention methods. Conclusion : Interventions were categorized into computer-based, task-oriented, sensory-motor, and integrated interventions. Task-oriented interventions were the most common in both domestic and international studies, while integrated interventions were the most effective. Based on these findings, it is necessary to increase awareness of the need for handwriting intervention research among occupational therapists in Korea. Additionally, there is a need for well-supported handwriting intervention research with larger sample sizes in both domestic and international occupational therapy. Finally, future research should actively investigate the application of tailored integrated interventions for school-aged children with handwriting difficulties.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.101-110
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• 2024

Purpose : The purpose of this study provided an overview of the general status and recent intervention approaches in overseas research related to feeding problems in children with autism spectrum disorder (ASD). This review aims to explore interventions for feeding problems in order to provide higher quality follow-up research directions and implications, particularly focusing on providing recommendations for future research in the context of domestic studies. Methods : Analyzing studies published in international journals from 2017 to 2023. This review involved six selected articles, through Embase, Pubmed, RISS, KISS database search engine. A literature analysis that includes inclusion and exclusion criteria, six selected articles were examined. The literature analysis categorized the general status of the research and intervention approaches and treatment components into intervention, treatment settings and therapists, and dependent variables, respectively. Results : Among feeding intervention approaches, parent education interventions based on behavioral therapy had the highest proportion, followed by multidisciplinary interventions. To maintain the effectiveness of interventions over the long term and to generalize them to the home environment, parent education that utilizes parents as mediators is considered a crucial factor. The most commonly observed effects as dependent variables were changes in the consumption of disliked foods, health foods and alterations in feeding behavior. Conclusion : This study introduces various intervention approaches for addressing feeding problems in children with autism spectrum disorder (ASD), focusing on the positive effects demonstrated by active intervention research in abroad. Furthermore, it underscores the need for additional research in Korea to validate the efficacy of these feeding intervention methods. Lastly, the study outlines future research directions aimed at developing feeding programs to support children with ASD and their families coping with feeding issues.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.253-259
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• 2024

This paper proposes a new speech enhancement model based on a complex nested U-Net with a dual-branch decoder. The proposed model consists of a complex nested U-Net to simultaneously estimate the magnitude and phase components of the speech signal, and the decoder has a dual-branch decoder structure that performs spectral mapping and time-frequency masking in each branch. At this time, compared to the single-branch decoder structure, the dual-branch decoder structure allows noise to be effectively removed while minimizing the loss of speech information. The experiment was conducted on the VoiceBank + DEMAND database, commonly used for speech enhancement model training, and was evaluated through various objective evaluation metrics. As a result of the experiment, the complex nested U-Net-based speech enhancement model using a dual-branch decoder increased the Perceptual Evaluation of Speech Quality (PESQ) score by about 0.13 compared to the baseline, and showed a higher objective evaluation score than recently proposed speech enhancement models.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.909-914
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• 2024

This study suggests a quantitative evaluation of transfer learning, which is widely used in various AI fields, including image recognition for robot vision. Quantitative and qualitative analyses of results applying transfer learning are presented, but transfer learning itself is not discussed. Therefore, this study proposes a quantitative evaluation of transfer learning itself based on MNIST, a handwritten digit database. For the reference network, the change in recognition accuracy according to the depth of the transfer learning frozen layer and the ratio of transfer learning data and pre-training data is tracked. It is observed that when freezing up to the first layer and the ratio of transfer learning data is more than 3%, the recognition accuracy of more than 90% can be stably maintained. The transfer learning quantitative evaluation method of this study can be used to implement transfer learning optimized according to the network structure and type of data in the future, and will expand the scope of the use of robot vision and image analysis AI in various environments.