• Steel and Composite Structures
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• 제39권4호
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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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• 제76권5호
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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.

• Steel and Composite Structures
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• 제39권1호
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• pp.81-93
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• 2021

Splitting tensile strength (STS) is an important mechanical parameter of concrete. This study offers novel methodologies for the early prediction of this parameter. Artificial neural network (ANN), which is a leading predictive method, is synthesized with two metaheuristic algorithms, namely atom search optimization (ASO) and equilibrium optimizer (EO) to achieve an optimal tuning of the weights and biases. The models are applied to data collected from the published literature. The sensitivity of the ASO and EO to the population size is first investigated, and then, proper configurations of the ASO-NN and EO-NN are compared to the conventional ANN. Evaluating the prediction results revealed the excellent efficiency of EO in optimizing the ANN. Accuracy improvements attained by this algorithm were 13.26 and 11.41% in terms of root mean square error and mean absolute error, respectively. Moreover, it raised the correlation from 0.89958 to 0.92722. This is while the results of the conventional ANN were slightly better than ASO-NN. The EO was also a faster optimizer than ASO. Based on these findings, the combination of the ANN and EO can be an efficient non-destructive tool for predicting the STS.

• Ocean Systems Engineering
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• 제11권1호
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• pp.1-16
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• 2021

Experimental and numerical investigations were conducted to study the performance of a surface-fixed horizontal porous wave barrier in regular waves. The characteristics of the reflection and transmission coefficients, energy dissipation, and vertical wave force were examined versus different porosities of the barrier. Numerical simulations based on 3D Reynolds Averaged Navier-Stokes equations with standard low-Re k-ε turbulent closure and volume of fluid approach were accomplished and compared with the experimental results conducted in a 2D wave tank. Experimental measurements and numerical simulations were shown to be in satisfactory agreement. The qualitative wave behavior propagating over a horizontal porous barrier such as wave run-up, wave breaking, air entrapment, jet flow, and vortex generation was reproduced by CFD computation. Through the discrete harmonic decomposition of the vertical wave force on a wave barrier, the nonlinear characteristics were revealed quantitatively. It was concluded that the surface-fixed horizontal barrier is more effective in dissipating wave energy in the short wave period region and more energy conversion was observed from the first harmonic to higher harmonics with the increase of porosity. The present numerical approach will provide a predictive tool for an accurate and efficient design of the surface-fixed horizontal porous wave barrier.

• ETRI Journal
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• 제44권4호
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2241-2258
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• 2023

This study developed digital literacy instruments and tested their effectiveness on college students' perceptions of AI technologies. In creating a new digital literacy test tool, we reviewed the concept and scale of digital literacy based on previous studies that identified the characteristics and measurement of AI literacy. We developed 23 preliminary questions for our research instrument and used a quantitative approach to survey 318 undergraduates. After conducting exploratory and confirmatory factor analysis, we found that digital literacy in the age of AI had four ability sub-factors: critical understanding, artificial intelligence social impact recognition, artificial intelligence technology utilization, and ethical behavior. Then we tested the sub-factors' predictive powers on the perception of AI's usefulness and ease of use. The regression result shows that the most common powerful predictor of the usefulness and ease of use of AI technology was the ability to use AI technology. This finding implies that for college students, the ability to use various tools based on AI technology is an essential competency in the AI era.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제27권4호
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• pp.324-341
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• 2023

Topological data analysis (TDA) is a data analysis technique, recently developed, that investigates the overall shape of a given dataset. The mapper algorithm is a TDA method that considers the connectivity of the given data and converts the data into a mapper graph. Compared to persistent homology, another popular TDA tool, that mainly focuses on the homological structure of the given data, the mapper algorithm is more of a visualization method that represents the given data as a graph in a lower dimension. As it visualizes the overall data connectivity, it could be used as a prediction method that visualizes the new input points on the mapper graph. The existing mapper packages such as Giotto-TDA, Gudhi and Kepler Mapper provide the descriptive mapper algorithm, that is, the final output of those packages is mainly the mapper graph. In this paper, we develop a simple predictive algorithm. That is, the proposed algorithm identifies the node information within the established mapper graph associated with the new emerging data point. By checking the feature of the detected nodes, such as the anomality of the identified nodes, we can determine the feature of the new input data point. As an example, we employ the fraud credit card transaction data and provide an example that shows how the developed algorithm can be used as a node prediction method.

• Genomics & Informatics
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• 제21권3호
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• pp.28.1-28.13
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• 2023

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

• International Journal of Stem Cells
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• 제17권2호
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• pp.120-129
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• 2024

Recent amendments to regulatory frameworks have placed a greater emphasis on the utilization of in vitro testing platforms for preclinical drug evaluations and toxicity assessments. This requires advanced tissue models capable of accurately replicating liver functions for drug efficacy and toxicity predictions. Liver organoids, derived from human cell sources, offer promise as a reliable platform for drug evaluation. However, there is a lack of standardized quality evaluation methods, which hinders their regulatory acceptance. This paper proposes comprehensive quality standards tailored for liver organoids, addressing cell source validation, organoid generation, and functional assessment. These guidelines aim to enhance reproducibility and accuracy in toxicity testing, thereby accelerating the adoption of organoids as a reliable alternative or complementary tool to animal testing in drug development. The quality standards include criteria for size, cellular composition, gene expression, and functional assays, thus ensuring a robust hepatotoxicity testing platform.

• 대한핵의학회지
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• 제38권6호
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• pp.492-497
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• 2004

목적: 여성에게 많이 발생되는 유방 종괴의 악성여부의 판단은 매우 중요하다. 기존의 여러 영상진단법으로도 유방 종괴의 악성여부를 구분하기가 어려운 경우가 많아 이에 대한 연구는 매우 많다. 이에 저자들은 유방 종괴의 악성여부를 평가하는데 많이 사용되고 있는 유방촬영술과 Tc-99m MIBI 유방스캔의 유용성을 알아보고, 조직병리 검사소견과의 연관성에 대해서도 함께 비교하여 보았다. 대상 및 방법: 유방종괴를 갖고 있는 여자 환자 총 80명(나이: 24-72세, 평균: 48.4세)을 대상으로 하였다. 모든 환자에서 유방촬영술과 Tc-99m MIBI 유방스캔을 시행하였다. 방사성동위원소 투여 후 10 분째에 조기 영상, 2시간째 지연 영상을 전면상과 양측면상으로 얻었다. 두 명의 숙련된 방사선과 전문의와 핵의학 전문의가 유방 종괴의 악성여부를 판독하였으며, 유방스캔의 정량적인 분석으로 방사능집적을 보이는 경우 각각의 종괴 대 배후방사능비와 배설율을 구하였다. 모든 종괴에서 조직 병리검사를 시행하였다. 각각에서 얻어진 결과치와 조직검사 결과와의 연관성을 chi-square와 상관분석을 이용하여 통계 분석하였다. 결과: 유방촬영술의 원발성 유방암 진단의 예민도, 특이도, 양성예측율, 음성예측율은 각각 87.5%, 56.3%, 75.0%, 75.0%이었다. 유방스캔에서는 총 80 명의 환자 중 45 명의 환자를 악성 종괴에 의한 방사능집적으로 판단하였다. 이중 41 명이 조직 검사에서 악성으로 판정되었고, 4명이 양성으로 판정되었다. 결과적으로 유방스캔의 예민도, 특이도, 양성예측율, 음성예측율은 각각 85.4%, 87.5%, 91.1%, 80.8%로 측정되었고, 유방촬영술 결과와 비교하여 예민도는 낮았으나 특이도, 양성예측율, 음성예측율은 모두 높았다. 양성 종괴의 종괴 대 배후방사능비는 관심영역의 10 분째 평균치에서 $1.409{\pm}0.30$, 2 시간째에서 $1.267{\pm}0.42$이었고, 10 분째 최대치에서 $1.604{\pm}0.42$, 2 시간째 최대치에서 $1.476{\pm}0.50$이었다. 악성 종괴의 종괴 대 배후방사능비는 10 분째 평균치에서 $2.220{\pm}1.07$, 2 시간째 평균치에서 $1.842{\pm}0.75$이었고, 10 분째 최대치에서 $2.993{\pm}1.94$, 2 시간째 최대치에서 $2.480{\pm}1.34$이었다. 10분과 2시간째의 종괴 대 배후방사능비의 양성과 악성의 차이는 모두 통계적으로 유의하였다. 결론: 유방촬영술은 원발성 유방암의 진단 예민도는 높았으나, 유방 종괴의 악성 여부 판정에 있어서는 Tc-99m MIBI를 이용한 유방스캔이 매우 유용하였다. 특히 종괴 대 배후방사능비를 이용할 경우 유방암의 진단율을 더욱 높힐 수 있었다.