• Corrosion Science and Technology
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• v.16 no.3
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• pp.151-159
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• 2017

The MA8 alloy (formula Mg-Mn-Се) has been shown to have greater corrosion stability than the VMD10 magnesium alloy (formula Mg-Zn-Zr-Y) in chloride-containing solutions by Scanning Vibrating Electrode Technique (SVET) and by optical microscopy, gravimetry, and volumetry. It has been established that the crucial factor for the corrosion activity of these samples is the occurrence of microgalvanic coupling at the sample surface. The peculiarities of the kinetics and mechanism of the corrosion in the local heterogeneous regions of the magnesium alloy surface were investigated by localized electrochemical techniques. The stages of the corrosion process in artificial defects in the coating obtained by plasma electrolytic oxidation (PEO) at the surface of the MA8 magnesium alloy were also studied. The analysis of the experimental data enabled us to determine that the corrosion process in the defect zone develops predominantly at the magnesium/coating interface. Based on the measurements of the corrosion rate of the samples with PEO and composite polymer-containing coatings, the best anticorrosion properties were displayed by the composite polymer-containing coatings.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.374-376
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• 2022

Community detection is meaningful research in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper proposes a method to detect community by detecting maximal cliques and obtain the high influence cliques by high influence nodes, then merge the cliques with high similarity in social network.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.322-326
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• 2023

We propose a method of movie recommendation that involves an algorithm known as spectral bipartition. The Social Network is constructed manually by considering the similar movies viewed by users in MovieLens dataset. This kind of similarity establishes implicit ties between viewers. Because we assume that there is a possibility that there might be a connection between users who share the same set of viewed movies. We cluster users by applying a community detection algorithm based on the spectral bipartition. This study helps to uncover the hidden relationships between users and recommend movies by considering that feature.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.406-409
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• 2023

With the explosion of information, recommendation algorithms are becoming increasingly important in providing people with appropriate content, enhancing their online experience. In this paper, we propose a recommender system using advanced deep reinforcement learning(DRL) techniques. This method is more adaptive and integrative than traditional methods. We selected the MovieLens dataset and employed the precision metric to assess the effectiveness of our algorithm. The result of our implementation outperforms other baseline techniques, delivering better results for Top-N item recommendations.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.389-391
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• 2023

In the era of Big Data, humanity is facing a huge overflow of information. To overcome such an obstacle, many new cutting-edge technologies are being introduced. The movie recommendation system is also one such technology. To date, many theoretical and practical kinds of research have been conducted. Our research also focuses on the movie recommendation system by implementing methods from Social Network Analysis(SNA) and Parallel Programming. We applied the Girvan-Newman algorithm to detect communities of users, and a future package to perform the parallelization. This approach not only tries to improve the accuracy of the system but also accelerates the execution time. To do our experiment, we used the MovieLense Dataset.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.377-379
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• 2022

POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.357-359
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• 2023

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.

• Journal of Veterinary Science
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• v.24 no.6
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• pp.85.1-85.13
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• 2023

Background: A recent increase in the occurrence of canine skin and soft tissue infections, including otitis externa and pyoderma, caused by antimicrobial-resistant Staphylococcus pseudintermedius and S. schleiferi has become a significant public and veterinary health issues. Objective: We investigated the virulence potentials associated with the occurrence of canine otitis externa in S. pseudintermedius and S. schleiferi. Methods: In this study, the prevalence of genes encoding leukocidins, exfoliative toxins, and staphylococcal enterotoxins (SEs) was investigated using previously characterized S. pseudintermedius (n = 26) and S. schleiferi (n = 19) isolates derived from canine otitis externa. Susceptibility to cathelicidins (K9CATH and PMAP-36) and hydrogen peroxide (H₂O₂) was also examined in both staphylococcal species. Results: A high prevalence of genes encoding leukocidins (lukS/F-I, lukS1/F1-S, and lukS2/F2-S), exfoliative toxins (siet, expB, and sset), and SEs was identified in both S. pseudintermedius and S. schleiferi isolates. Notably, S. pseudintermedius isolates possessed higher number of SE genes, especially newer SE genes, than S. schleiferi isolates harboring egc clusters. Although no significant differences in susceptibility to K9CATH and H₂O₂ were observed between the two isolate groups, S. pseudintermedius isolates exhibited enhanced resistance to PMAP-36 compared to S. schleiferi isolates. Conclusions: These findings suggest that high a prevalence of various toxin genes together with enhanced resistance to cathelicidins may contribute to the pathogenicity of S. pseudintermedius and S. schleiferi in canine cutaneous infections.