• Education of Primary School Mathematics
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• v.26 no.4
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• pp.317-332
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• 2023

Mathematical modeling activities hold the potential for diverse applications, involving the transformation of real-life situations into mathematical models to facilitate problem-solving. In order to assess the cognitive, affective, and social dimensions of students' engagement in mathematical modeling activities, this study conducted sessions with ten groups of fifth-grade elementary school students. The ensuing processes and outcomes were thoroughly analyzed. As a result, each group effectively applied mathematical concepts and principles in creating mathematical models and gathering essential information to address real-world tasks. This led to notable shifts in interest, enhanced mathematical proficiency, and altered attitudes towards mathematics, all while promoting increased collaboration and communication among group members. Based on these analytical findings, the study offers valuable pedagogical insights and practical guidance for effectively implementing mathematical modeling activities.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.112-118
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• 2023

As technology advances, the need for enhanced preparedness against cyber-attacks becomes an increasingly critical problem. Therefore, it is imperative to consider various circumstances and to prepare for cyber-attack strategic technology. This paper proposes a method to solve network security problems by applying reinforcement learning to cyber-security. In general, traditional static cyber-security methods have difficulty effectively responding to modern dynamic attack patterns. To address this, we implement cyber-attack scenarios such as 'Tiny Alpha' and 'Small Alpha' and evaluate the performance of various reinforcement learning methods using Network Attack Simulator, which is a cyber-attack simulation environment based on the gymnasium (formerly Open AI gym) interface. In addition, we experimented with different RL algorithms such as value-based methods (Q-Learning, Deep-Q-Network, and Double Deep-Q-Network) and policy-based methods (Actor-Critic). As a result, we observed that value-based methods with discrete action spaces consistently outperformed policy-based methods with continuous action spaces, demonstrating a performance difference ranging from a minimum of 20.9% to a maximum of 53.2%. This result shows that the scheme not only suggests opportunities for enhancing cybersecurity strategies, but also indicates potential applications in cyber-security education and system validation across a large number of domains such as military, government, and corporate sectors.

• Journal of Sport and Applied Science
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• v.8 no.1
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• pp.11-17
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• 2024

Purpose: The purpose of the study was to understand a trend of esports in terms of gamers' and fans' perceptions toward esports using social big data. Research design, data, and methodology: In this study, researchers first selected keywords related to esports. Then a total of 10,138 buzz data created at twitter, Facebook, news media, blogs, café and community between November 10, 2022 and November 19, 2023 were collected and analyzed with 'Textom', a big data solution. Results: The results of this study were as follows. Firstly, the news data's main articles were about competitions hosted by local governments and policies to revitalize the gaming industry. Secondly, As a result of esports analysis using Textom, there was a lot of interest in the adoption of the Hangzhou Asian Games as an official event and various esports competitions. As a result of the sentiment analysis, the positive content was related to the development potential of the esports industry, and the negative content was a discussion about the fundamental problem of whether esports is truly a sport. Thirdly, As a result of analyzing social big data on esports and the Olympics, there was hope that it would be adopted as an official event in the Olympics due to its adoption as an official event in the Hangzhou Asian Games. Conclusions: There was a positive opinion that the adoption of esports as an official Olympic event had positive content that could improve the quality of the game, and a negative opinion that games with actions that violate the Olympic spirit, such as murder and assault, should not be adopted as an official Olympic event. Further implications were discussed.

• Animal Bioscience
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• v.37 no.5
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• pp.918-928
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• 2024

Objective: The adulteration of raw beef (BMr) with dog meat (DMr) and pork (PMr) becomes a serious problem because it is associated with halal status, quality, and safety of meats. This research aimed to develop an effective authentication method to detect non-halal meats (dog meat and pork) in beef using metabolomics approach. Methods: Liquid chromatography-high resolution mass spectrometry (LC-HRMS) using untargeted approach combined with chemometrics was applied for analysis non-halal meats in BMr. Results: The untargeted metabolomics approach successfully identified various metabolites in BMr DMr, PMr, and their mixtures. The discrimination and classification between authentic BMr and those adulterated with DMr and PMr were successfully determined using partial least square-discriminant analysis (PLS-DA) with high accuracy. All BMr samples containing non-halal meats could be differentiated from authentic BMr. A number of discriminating metabolites with potential as biomarkers to discriminate BMr in the mixtures with DMr and PMr could be identified from the analysis of variable importance for projection value. Partial least square (PLS) and orthogonal PLS (OPLS) regression using discriminating metabolites showed high accuracy (R² >0.990) and high precision (both RMSEC and RMSEE <5%) in predicting the concentration of DMr and PMr present in beef indicating that the discriminating metabolites were good predictors. The developed untargeted LC-HRMS metabolomics and chemometrics successfully identified non-halal meats adulteration (DMr and PMr) in beef with high sensitivity up to 0.1% (w/w). Conclusion: A combination of LC-HRMS untargeted metabolomic and chemometrics promises to be an effective analytical technique for halal authenticity testing of meats. This method could be further standardized and proposed as a method for halal authentication of meats.

• Journal of Korean Society on Water Environment
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• v.40 no.1
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• pp.36-46
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• 2024

Microplastics in water resources have been recognized as a serious problem. The discharge of microplastics from wastewater treatment plants is considered a major contributor to environmental pollution in water resources. However, a reliable analytical method for quantifying microplastics in wastewater treatment plants has not yet been established. This study proposes a reliable, quick, and easy analytical method for quantifying microplastics. For the removal of organic particles, preprocessing steps were applied including oxidation, sonication, washing, and sieving. Nile Red staining was used to visualize microplastics, and quantitative analysis was conducted using fluorescent imaging. The stained microplastics were ultimately quantified through image analysis software. Among the preprocessing steps, sonication and washing stages were particularly effective in efficiently removing interfering substances from wastewater, enhancing the accuracy of the microplastic analysis. Additionally, various solvents (methanol, acetone, and N-hexane) for the Nile Red staining solution were tested. When N-hexane was applied as the solvent, the quantity of stained microplastics was lower compared to methanol and acetone. This suggests that N-hexane has a greater potential of reducing false staining and counting of non-plastic particles. In summary, this research demonstrates a robust method for quantifying microplastics in wastewater treatment plants by employing effective preprocessing steps and optimizing the staining process with Nile Red and N-hexane.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.20 no.1
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• pp.16-24
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• 2024

Denting of steam generator (SG) tube is defined as the reduction in tube diameter due to the stresses exerted by the corrosion products formed on the outer diameter surface. This phenomenon is mostly observed in the crevices between SG tube and the top-of tubesheet or tube support plate. Despite the replacement of SG tube with Alloy 690, which has better corrosion resistance than Alloy 600, the denting of SG tube still remains a potential problem that could decrease the SG integrity. Deformation of SG tube by denting phenomenon can affect the surface properties and microstructure of SG tube. In this study, the effects of plastic deformation on surface properties and microstructure of Alloy 690 thermally treated (TT) tube was investigated by using the various analysis techniques. The plastic deformation of Alloy 690 increased the surface roughness and area. Many surface defects such as ripped surface and micro-cracks were observed on the deformed Alloy 690TT specimen. Based on the electron backscatter diffraction analysis, the dislocation density of deformed SG tube increased compared to non-deformed SG tube. In addition, the effects of changes in surface properties and microstructure of SG tube on general corrosion behavior were discussed.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.119-126
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• 2024

In building information model (BIM), it is difficult to train an artificial intelligence (AI) model due to the lack of sufficient data about individual projects in an architecture firm. In this paper, we present a methodology to correctly train an AI neural network model based on a large language model (LLM) to predict the steel structure product weight ratios in BIM. The proposed method, with the aid of the LLM, can overcome the inherent problem of limited data availability in BIM and handle a combination of natural language and numerical data. The experimental results showed that the proposed method demonstrated significantly higher accuracy than methods based on a smaller language model. The potential for effectively applying large language models in BIM is confirmed, leading to expectations of preventing building accidents and efficiently managing construction costs.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.467-474
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• 2024

This study analyses customer data from a US online lending platform to empirically document the discriminatory treatment that low- and middle-income borrowers face in financial markets. Researchers are using financial data from nearly 2.93 million loans between 2007~2020 of the Lending Club on the open-source Kaggle platform. We find that thin-filers borrowers, especially those with lower credit scores, receive loans at higher interest rates. This discriminatory treatment undermines financial inclusion and has the potential to increase social inequality. The significance of this research is that it sheds substantial light on the problem of inequality in financial markets and, based on the findings, suggests concrete measures to ensure equitable access to finance for all customers and enhance sustainable financial inclusion. In doing so, we propose a shift towards enhancing the social responsibility of institutions.

• Nuclear Engineering and Technology
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• v.56 no.9
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• pp.3571-3584
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• 2024

Reactor core transient calculation is very important for the reactor safety analysis, in which the kernel is neutron kinetics calculation by simulating the variation of neutron density or thermal power over time. Compared with the point kinetics method, the time-space neutron kinetics calculation can provide accurate variation of neutron density in both space and time domain. But it consumes a lot of resources. It is necessary to develop a surrogate model that can quickly obtain the temporal and spatial variation information of neutron density or power with acceptable calculation accuracy. This paper uses the time-varying characteristics of power to construct a time function, parameterizes the time-varying characteristics which contains the information about the spatial change of power. Thereby, the amount of targets to predict in the space domain is compressed. A surrogate method using the machine learning is proposed in this paper. In the construction of a neural network, the input is processed by a convolutional layer, followed by a fully connected layer or a deconvolution layer. For the problem of time sequence disturbance, a structure combining convolutional neural network and recurrent neural network is used. It is verified in the tests of a series of 1D, 2D and 3D reactor models. The predicted values obtained using the constructed neural network models in these tests are in good agreement with the reference values, showing the powerful potential of the surrogate models.

• Journal of the korean academy of Pediatric Dentistry
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• v.32 no.4
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• pp.595-603
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• 2005

Incomplete removal of bacteria contaminated dentin or enamel associated with caries is a potential problem in restorative dentistry Secondary or residual caries, pulpal inflammation and hypersensitivity may result from bacteria left after the initial preparation, especially if an adequate seal against microleakage is not obtained. A possible solution to eliminate residual bacteria left in a cavity preparation would be to treat the cavity with cavity disinfectant wash. But a potential problem with using a cavity disinfectant with dentin bonding agents could be their interference with the ability of the resin to bond to the tooth micromechanically. The purpose of this study was to evaluate the effect of 2% chlorhexidine containing cavity disinfectant ($Consepsis^{(R)}$) on shear bond strength and microleakage of dentin bonding agents, $Adper ^{TM}$ $Scotchbond^{TM}$ Multi-Purpose, $Adper^{TM}$ Single Bond and $Adper^{TM}\;Prompt^{TM}\; L-Pop^{TM}$ Sixty and sixty sound human third molar teeth, respectively, were used for shear bond strength and microleakage test. For experimental group, cavity disinfectant was applied before dentin bonding agents, and was not applied for the control group. The result from the this study can be summarized as follows ; 1. Use of 2% chlorhexidine containing cavity disinfectant($Consepsis^{(R)}$) does not significantly affect the shear bond strength of dentin bonding agents. 2. Use of 2% chlorhexidine containing cavity disinfectant($Consepsis^{(R)}$) does not significantly affect the microleakage of dentin bonding agents.