• Steel and Composite Structures
• /
• v.49 no.3
• /
• pp.337-348
• /
• 2023

Successive wetting and drying cycles of concrete due to weather changes can endanger the safety of engineering structures over time. Considering wetting and drying cycles in concrete tests can lead to a more correct and reliable design of engineering structures. This study aims to provide a model that can be used to estimate the resistance properties of concrete under different wetting and drying cycles. Complex sample preparation methods, the necessity for highly accurate and sensitive instruments, early sample failure, and brittle samples all contribute to the difficulty of measuring the strength of concrete in the laboratory. To address these problems, in this study, the potential ability of six machine learning techniques, including ANN, SVM, RF, KNN, XGBoost, and NB, to predict the concrete's tensile strength was investigated by applying 240 datasets obtained using the Brazilian test (80% for training and 20% for test). In conducting the test, the effect of additives such as glass and polypropylene, as well as the effect of wetting and drying cycles on the tensile strength of concrete, was investigated. Finally, the statistical analysis results revealed that the XGBoost model was the most robust one with R2 = 0.9155, mean absolute error (MAE) = 0.1080 Mpa, and variance accounted for (VAF) = 91.54% to predict the concrete tensile strength. This work's significance is that it allows civil engineers to accurately estimate the tensile strength of different types of concrete. In this way, the high time and cost required for the laboratory tests can be eliminated.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.5
• /
• pp.999-1008
• /
• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Journal of Advanced Technology Convergence
• /
• v.3 no.2
• /
• pp.33-38
• /
• 2024

Productive welfare is a form of welfare policy that helps welfare recipients move beyond being mere objects of support and develop into members who can actively contribute to society and the economy. This aims to improve individual self-reliance, including education, vocational training, and employment support services, ultimately reducing the economic burden on society as a whole and promoting economic growth. By examining whether productive welfare is possible in Korea, this study emphasizes the role and importance of productive welfare as a solution to social and economic problems. The Republic of Korea has experienced various social problems along with rapid economic growth. The entry into an aging society, increased youth unemployment, and widening social gaps have created complex and diverse social welfare needs. In this situation, productive welfare is attracting attention as a method that goes beyond simple financial support and provides a foundation for beneficiaries to become self-reliant. This study seeks to present a new horizon for social welfare policy by examining the possibility of implementing productive welfare in Korea and exploring ways to achieve it.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.3
• /
• pp.87-94
• /
• 2024

We investigated the feasibility of utilizing Large Language Models (LLMs) to perform text-based games without training on game data in advance. We adopted ChatGPT-3.5 and its state-of-the-art, ChatGPT-4, as the systems that implemented LLM. In addition, we added the persistent memory feature proposed in this paper to ChatGPT-4 to create three game player agents. We used Zork, one of the most famous text-based games, to see if the agents could navigate through complex locations, gather information, and solve puzzles. The results showed that the agent with persistent memory had the widest range of exploration and the best score among the three agents. However, all three agents were limited in solving puzzles, indicating that LLM is vulnerable to problems that require multi-level reasoning. Nevertheless, the proposed agent was still able to visit 37.3% of the total locations and collect all the items in the locations it visited, demonstrating the potential of LLM.

• Journal of Hospice and Palliative Care
• /
• v.27 no.1
• /
• pp.1-10
• /
• 2024

This article underscores the importance of integrating comprehensive palliative care for noncancer patients who are undergoing hemodialysis, with an emphasis on the aging populations in Asian nations such as Taiwan, Japan, the Republic of Korea, and China. As the global demographic landscape shifts towards an aging society and healthcare continues to advance, a marked increase has been observed in patients undergoing hemodialysis who require palliative care. This necessitates an immediate paradigm shift to incorporate this care, addressing the intricate physical, psychosocial, and spiritual challenges faced by these individuals and their families. Numerous challenges impede the provision of effective palliative care, including difficulties in prognosis, delayed referrals, cultural misconceptions, lack of clinician confidence, and insufficient collaboration among healthcare professionals. The article proposes potential solutions, such as targeted training for clinicians, the use of telemedicine to facilitate shared decision-making, and the introduction of time-limited trials for dialysis to overcome these obstacles. The integration of palliative care into routine renal treatment and the promotion of transparent communication among healthcare professionals represent key strategies to enhance the quality of life and end-of-life care for people on hemodialysis. By embracing innovative strategies and fostering collaboration, healthcare providers can deliver more patient-centered, holistic care that meets the complex needs of seriously ill patients within an aging population undergoing hemodialysis.

• Korean Journal of Clinical Laboratory Science
• /
• v.56 no.1
• /
• pp.89-98
• /
• 2024

Gross examination techniques (GETs) of specimens collected from cancer surgery or endoscopy comprise the act of recording visual information about cancer for accurate histopathological diagnosis and collecting sections of the lesion to create microscopic specimens. GETs must include concise and accurate expressions, appropriate structuring, sufficient resections, error-free standardization of important information, and photo-diagramming of complex specimens. To increase the satisfaction of pathological interpretation, it is a task that must be performed accurately and carefully to gain confidence on a theoretical and practical basis with a sufficient understanding of gross examination. Based on the experience of clinical pathologists in the field of GETs, additional specimen types should be identified as viable candidates. Also, their needs and concerns regarding treatment should be carefully considered. In addition, departments at each institution should review the national focus on clinical partnerships, continuous professional training, diagnostic errors, and value-based healthcare provision.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.34 no.1
• /
• pp.143-156
• /
• 2024

Federated Learning (FL) has emerged as a potent methodology for decentralized model training across multiple collaborators, eliminating the need for data sharing. Although FL is lauded for its capacity to preserve data privacy, it is not impervious to various types of privacy attacks. Differential Privacy (DP), recognized as the golden standard in privacy-preservation techniques, is widely employed to counteract these vulnerabilities. This paper makes a specific contribution by applying an existing, task-specific adaptive DP mechanism to the FL environment. Our comprehensive analysis evaluates the impact of this mechanism on the performance of a shared global model, with particular attention to varying data distribution and partitioning schemes. This study deepens the understanding of the complex interplay between privacy and utility in FL, providing a validated methodology for securing data without compromising performance.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.44 no.3
• /
• pp.397-405
• /
• 2024

In order to improve the visibility of nighttime thermal infrared images, a simulation method with daytime color images was proposed. As a simulation method consisting of two steps, the daytime thermal infrared image was simulated by learning the unpaired nighttime thermal infrared image and daytime thermal infrared image, then the result was translated into a daytime color image. A temperature change regression equation was constructed and applied to reflect the systematic characteristics of temperature changes in daytime and nighttime images, and day and night simulation and colorization were trained and modeled by CycleGAN. For the experimental area, 100 images were captured and used for training. As a result, the simulation showed an average SSIM of 0.2449 and a PSNR of 51.2254. It was confirmed that the method could simulate complex and detailed features such as vegetation.

• Computers and Concrete
• /
• v.34 no.2
• /
• pp.247-265
• /
• 2024

Measuring the fracture toughness of concrete in laboratory settings is challenging due to various factors, such as complex sample preparation procedures, the requirement for precise instruments, potential sample failure, and the brittleness of the samples. Therefore, there is an urgent need to develop innovative and more effective tools to overcome these limitations. Supervised learning methods offer promising solutions. This study introduces seven machine learning algorithms for predicting concrete's effective fracture toughness (K-eff). The models were trained using 560 datasets obtained from the central straight notched Brazilian disc (CSNBD) test. The concrete samples used in the experiments contained micro silica and powdered stone, which are commonly used additives in the construction industry. The study considered six input parameters that affect concrete's K-eff, including concrete type, sample diameter, sample thickness, crack length, force, and angle of initial crack. All the algorithms demonstrated high accuracy on both the training and testing datasets, with R² values ranging from 0.9456 to 0.9999 and root mean squared error (RMSE) values ranging from 0.000004 to 0.009287. After evaluating their performance, the gated recurrent unit (GRU) algorithm showed the highest predictive accuracy. The ranking of the applied models, from highest to lowest performance in predicting the K-eff of concrete, was as follows: GRU, LSTM, RNN, SFL, ELM, LSSVM, and GEP. In conclusion, it is recommended to use supervised learning models, specifically GRU, for precise estimation of concrete's K-eff. This approach allows engineers to save significant time and costs associated with the CSNBD test. This research contributes to the field by introducing a reliable tool for accurately predicting the K-eff of concrete, enabling efficient decision-making in various engineering applications.

• Journal of Microbiology and Biotechnology
• /
• v.34 no.9
• /
• pp.1836-1847
• /
• 2024

Polyethylene terephthalate (PET), one of the most widely used plastics in the world, causes serious environmental problems. Recently, scientists have been focused on the enzymatic degradation of PET, an environmentally friendly method that offers an attractive approach to the degradation and recycling of PET. In this work, PET hydrolase from Streptomyces sp. W2061 was biochemically characterized, and the biodegradation of PET was performed using the PET model substrate bis (2-hydroxyethyl terephthalate) (BHET). PET hydrolase has an isoelectric point of 5.84, and a molecular mass of about 50.31 kDa. The optimum pH and temperature were 7.0 and 40℃, respectively. LC-MS analysis of the enzymatic products showed that the PET hydrolase successfully degraded a single ester bond of BHET, leading to the formation of MHET. Furthermore, in silico characterization of the PET hydrolase protein sequence and its predicted three-dimensional structure was designed and compared with the well-characterized IsPETase from Ideonella sakaiensis. The structural analysis showed that the (Gly-x1-Ser-x2-Gly) serine hydrolase motif and the catalytic triad (Ser, Asp, and His) were conserved in all sequences. In addition, we integrated molecular dynamics (MD) simulations to analyze the variation in the structural stability of the PET hydrolase in the absence and presence of BHET. These simulations showed the formation of a stable complex between the PET hydrolase and BHET. To the best of our knowledge, this is the first study on Streptomyces sp. W2061 to investigate the BHET degradation activity of PET hydrolase, which has potential application in the biodegradation of plastics in the environment.