• Smart Structures and Systems
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• v.30 no.6
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• Nutrition Research and Practice
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• v.16 no.6
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• pp.789-800
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• 2022

BACKGROUND/OBJECTIVES: The aim of this study was to examine the validity and reproducibility of a food frequency questionnaire (FFQ) developed in Korea for breast cancer survivors. SUBJECTS/METHODS: Ninety-nine breast cancer survivors who completed an FFQ twice and three 3-day dietary records (DRs) between 2016-2017 were included. Energy and 14 nutrient intakes were calculated from FFQs and DRs. To determine the validity of the FFQ, energy-adjusted de-attenuated Pearson correlations between two FFQ assessments and the average of the three 3-day DRs were calculated, and to determine reproducibility, energy-adjusted Pearson correlations and degrees of agreement were calculated between the first and second FFQ assessments. RESULTS: Correlation coefficients of validity ranged from 0.29 (protein) to 0.47 (fat) (median value = 0.36) for the FFQ assessment and from 0.20 (riboflavin) to 0.53 (calcium) (median value = 0.37) for the second. Correlation coefficients of reproducibility ranged from 0.22 (sodium) to 0.62 (carbohydrate) (median value = 0.36). Regarding FFQ reproducibilities, percentage classifications of exact agreements for energy-adjusted nutrients ranged from 27.3% (sodium) and 45.5% (fat). A median 76.8% of participants were classified into the same or adjacent quartiles, while a median of 5.6% of participants were classified in extreme quartiles. Bland-Atman plots for the majority of data points of three macronutrients, calcium and vitamins A and C fell within limits of agreement. CONCLUSIONS: These results indicated that the newly developed FFQ for Korean breast cancer survivors has acceptable validity and reproducibility as compared with three 3-day DRs collected over a one-year period.

• Journal of Korean Academy of Dental Administration
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• v.10 no.1
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• pp.53-65
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• 2022

The Primary Care Patient Measure of Safety (PC-PMOS) is a measure of safety that allows patients to evaluate contributing factors related to patient safety in primary care. This study aimed to examine which questionnaire items of the PC-PMOS could be used in Korean dental institutions. A survey of the Korean translation of the PC-PMOS, a self-reported questionnaire, was conducted in August 2022 by 400 adults who had used dental-care services within the last three years. Of the 77 items, 34 were selected using principal component analysis and two experts' evaluations based on face validity. Five domains were identified from factor analysis: patient centeredness, patient information update, complaint processing, communication, and information about the complaint process. The Cronbach's alpha of the data was 0.913, indicating high reliability. As a result of the generalized multiple regression analysis, regression coefficients were not statistically significant, except for household income. This indicated that there was no bias in the patient safety scores of dental institutions evaluated by patients within the range of independent variables used in this study. The five domains with 34 items identified in this study suggested the factors that contribute to the safety of patients who used dental care services in Korea. However, validation of this study result is still important to refine questionnaires suitable for dental institutions in Korea so as to further improve the quality of dental care.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of Microbiology and Biotechnology
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• v.32 no.10
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• pp.1262-1274
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• 2022

Cholangiocarcinoma (CCA) is a complex and refractor type of cancer with global prevalence. Several barriers remain in CCA diagnosis, treatment, and prognosis. Therefore, exploring more biomarkers and therapeutic drugs for CCA management is necessary. CCA gene expression data was downloaded from the TCGA and GEO databases. KEGG enrichment, GO analysis, and protein-protein interaction network were used for hub gene identification. miRNA were predicted using Targetscan and validated according to several GEO databases. The relative RNA and miRNA expression levels and prognostic information were obtained from the GEPIA. The candidate drug was screened using pharmacophore-based virtual screening and validated by molecular modeling and through several in vitro studies. 301 differentially expressed genes (DEGs) were screened out. Complement and coagulation cascades-related genes (including AHSG, F2, TTR, and KNG1), and cell cycle-related genes (including CDK1, CCNB1, and KIAA0101) were considered as the hub genes in CCA progression. AHSG, F2, TTR, and KNG1 were found to be significantly decreased and the eight predicted miRNA targeting AHSG, F2, and TTR were increased in CCA patients. CDK1, CCNB1, and KIAA0101 were found to be significantly abundant in CCA patients. In addition, Molport-003-703-800, which is a compound that is derived from pharmacophores-based virtual screening, could directly bind to CDK1 and exhibited anti-tumor activity in cholangiocarcinoma cells. AHSG, F2, TTR, and KNG1 could be novel biomarkers for CCA. Molport-003-703-800 targets CDK1 and work as potential cell cycle inhibitors, thereby having potential for consideration for new chemotherapeutics for CCA.

• Journal of Engineering Education Research
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• v.25 no.4
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• pp.21-34
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• 2022

This study is conducted with the aim of identify the factors constituting learning competencies for engineering college students, and developing and validating the scale to measure them. To this end, literature and prior research were reviewed and focus group interview was conducted with high-achieving learners of K University in the capital region of Korea. According to previous research, 3 learning competency groups, 12 learning competencies and 41 sub-competencies were derived. Delphi survey was carried out twice, 28 sub-competencies were derived among the 41 sub-competencies through this process. 166 initial items were developed through literature review and FGI. Then, 130 items were confirmed by verifying content validity in the second Delphi survey. Based on this, pilot test were performed with 110 students in K university, and an interview was conducted with 50 students who participated in the pilot test. Reflecting the pilot test results, 1 sub-competency and 22 items were deleted. After the confirmed pilot test results, the main test were performed with all current students in K University. According to the main test result, the validity of the scale and the model fit was verified for the response data of 823 students, and the scale consisting of a total of 105 items was confirmed. The final learning competencies scale included three competency groups and 10 learning competencies. The scale developed in this study can be used as an independent scale for each competency group as needed. It is expected that this scale can be contributed to support the development their learning competencies for academic success of engineering college students, who are future learners.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.33 no.3
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• pp.67-72
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• 2022

Objectives: This study investigated the reliability and validity of the Korean version of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) Level 2 Cross-Cutting Symptom Measure-Patient-Reported Outcomes Measurement Information System (PROMIS)-Depression and the Irritability for parents of children aged 6-17 years. Methods: Participants were 190 children diagnosed with depressive disorder (n=14), anxiety disorder (n=21), attention-deficit/hyperactivity disorder (ADHD; n=111), ADHD with anxious depression (n=13), and tic disorder with somatic symptoms (n=31). Patients were 8-15 years of age. The participants' mothers completed the Korean versions of the DSM-5 Level 2 Cross-Cutting Symptom Measure-PROMIS Depression and Irritability (Affective Reactivity Index, ARI), and the Korean Child Behavior Checklist (K-CBCL). Using these data, we calculated the reliability coefficient and examined the concurrent and discriminant validity of the PROMIS Depression and the Irritability (ARI) scales for assessing depression and irritability in children. Results: The reliability coefficient of the PROMIS Depression scale (Cronbach's α) was 0.93. The correlation coefficient with the K-CBCL DSM emotional problem score was 0.71. The PROMIS Depression scale significantly discriminated children with depressive disorders from those with other conditions. The reliability coefficient of the Irritability (ARI) scale was 0.91, suggesting its high reliability. Conclusion: Our results suggest that the Korean version of the DSM-5 Level 2 Cross-Cutting Symptom Measure for Depression and Irritability Scales for parents of children aged 6-17 years is reliable and valid and may be an efficient alternative to the K-CBCL.

• Journal of Hospice and Palliative Care
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• v.25 no.3
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• pp.110-120
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• 2022

Purpose: The purpose of this study was to confirm the factor structure of the McGill Quality of Life Questionnaire-Revised (MQOL-R) in the context of Korean culture and to verify its reliability and validity. Methods: The participants comprised terminal cancer patients aged 25 or older, and data from 164 participants were analyzed. The study was conducted in the following order: translation, expert review, reverse translation, preliminary investigation and interviews, and completion of the final version. Confirmatory factor analysis was applied to evaluate the validity of the instrument, and the Beck Depression Inventory, Korean version (K-BDI) was applied to confirm the criterion validity of the MQOL-R Korean version. The Cronbach's alpha coefficient, representing internal consistency, was measured to evaluate reliability. Results: Cronbach's alpha for all 14 questions was 0.862. The model fit indices for confirmatory factor analysis were within the acceptance criteria. The factor loadings of all four factors were over 0.50, and convergent validity and discriminant validity were confirmed. Regarding criterion validity, a negative correlation was found between the four factors of MQOL-R Korean version and the K-BDI. Conclusion: The MQOL-R Korean version, the reliability and validity of which were verified in this study, is a 15-item tool consisting of 14 items dealing with four physical, psychological, existential, and social factors and a single item evaluating the overall quality of life. The MQOL-R Korean version is an instrument that can more concisely and effectively measure the quality of life of patients with life-threatening diseases.

• BMB Reports
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• v.55 no.6
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• pp.281-286
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• 2022

Hepatocellular carcinoma is a major health burden, and though various treatments through much research are available, difficulties in early diagnosis and drug resistance to chemotherapy-based treatments render several ineffective. Cancer stem cell model has been used to explain formation of heterogeneous cell population within tumor mass, which is one of the underlying causes of high recurrence rate and acquired chemoresistance, highlighting the importance of CSC identification and understanding the molecular mechanisms of CSC drivers. Extracellular CSC-markers such as CD133, CD90 and EpCAM have been used successfully in CSC isolation, but studies have indicated that increasingly complex combinations are required for accurate identification. Pseudogene-derived long non-coding RNAs are useful candidates as intracellular CSC markers - factors that regulate pluripotency and self-renewal - given their cancer-specific expression and versatile regulation across several levels. Here, we present the use of microarray data to identify stemness-associated factors in liver cancer, and selection of sole pseudogene-derived lncRNA ZNF204P for experimental validation. ZNF204P knockdown impairs cell proliferation and migration/invasion. As the cytosolic ZNF204P shares miRNA binding sites with OCT4 and SOX2, well-known drivers of pluripotency and self-renewal, we propose that ZNF204P promotes tumorigenesis through the miRNA-145-5p/OCT4, SOX2 axis.

• Earthquakes and Structures
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• v.23 no.5
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• pp.403-417
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• 2022

Reinforced concrete (RC) structures with low-strength RC columns are rampant in several countries, especially those constructed during the early 1960s and 1970s. The weakness of these structures due to overloading or some natural disasters such as earthquakes and building age effects are some of the main reasons to collapse, particularly with the scarcity of data on the impact of aspect ratio and corner radius on the confinement effectiveness. Hence, it is crucial to investigate if these columns (with different aspect ratios) can be made safe by strengthening them with carbon fiber-reinforced polymers (CFRP) sheets. Therefore, experimental and numerical studies of CFRP-strengthened low-strength reinforced concrete short rectangular, square, and circular columns were studied. In this investigation, a total of 6 columns divided into three sets were evaluated. The first set had two circular cross-sectional columns, the second set had two square cross-section columns, and the third set has two rectangular cross-section columns. Furthermore, FEM validation has been conducted for some of the experimental results obtained from the literature. The experimental results revealed that the confinement equations for RC columns as per both CSA and ACI codes could give incorrect results for low-strength concrete. The control specimen (unstrengthened ones) displayed that both ACI and CSA equations overestimate the ultimate strength of low-strength RC columns by order of extent. For strengthened columns with CFRP, the code equations of CSA and ACI code overestimate the maximum strength by around 6 to 13% and 23 to 29%, respectively, depending on the cross-section of the column (i.e., square, rectangular, or circular). Results of finite element models (FEMs) showed that increasing the layer number of new commonly CFRP type (B) from one to 3 for circular columns can increase the column's ultimate loads by around eight times compared to unjacketed columns. However, in the case of strengthened square and rectangular columns with CFRP, the increase of the ultimate loads of columns can reach up to six times and two times, respectively.