• Health Policy and Management
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• v.33 no.3
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• pp.338-354
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• 2023

Background: The influence of social capital on the spread of coronavirus disease 2019 (COVID-19) and related behaviors and perceptions has been recognized during the COVID-19 pandemic. This study aims to analyze the trends in social capital using primary data from the Korean Community Health Survey, which is the only available source in Korea for local-level social capital analysis. It also investigates the relationship between various variables, including social capital, as factors influencing COVID-19-related behaviors and perceptions. Methods: The study analyzed the temporal trends of social capital using raw data from four community health surveys conducted in 2017, 2019, 2020, and 2021. A multilevel analysis was conducted to examine the relationship between social capital and COVID-19-related behaviors and perceptions following the onset of the COVID-19 pandemic in 2020. Results: Social capital consists of trust, bonding social capital, and bridging social capital. Within the trust sub-factor, trust in neighbors (Trust-1) declined after the COVID-19 pandemic, whereas trust in safety and general environment (Trust-2) and trust in medical services and public transportation (Trust-3) increased. Additionally, the gap between municipalities narrowed. COVID-19-related behaviors and perceptions, such as adherence to COVID-19 prevention measures, return to normal activities, and fear of COVID-19, showed improvement in 2021 compared to the previous year. Individual-level trust in neighbors was associated with reduced fear of COVID-19, while community-level trust in neighbors was associated with increased fear of COVID-19. Conclusion: Social capital plays a role in mitigating public health crises, and it is necessary to implement active policies that address the gap in social capital between metropolitan and rural areas. Strengthening risk communication regarding emerging infectious diseases such as COVID-19 is crucial.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.5
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• pp.13-24
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• 2023

Due to recent climate change, the amount of rainfall during the summer season in South Korea has been decreasing, leading to an increase in areas affected by frequent droughts. Droughts have the characteristic of occurring over a wide area and being unpredictable in terms of their onset and end, necessitating proactive research to cope with them. In this study, we conducted an assessment of agricultural drought vulnerability in Taean-gun, Chungcheongnam-do, focusing on irrigation facilities and paddy fields. The assessment criteria were meteorological impact, drought occurrence status, supplementary water supply capacity, and drought response capability, with nine specific indicators selected. The drought response capability was analyzed by applying a scoring system as a key component of the agricultural drought vulnerability assessment, while the other indicators were quantified using an entropy weighting technique. The results of the assessment showed that Anmyeon-eup and Taean-eup were the safest areas, while Wonbuk-myeon, Nam-myeon, and Gonam-myeon were the most vulnerable. It is expected that the findings can be utilized to enhance understanding and proactive measures for coping with agricultural drought, and to determine the priority of drought response in different regions.

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

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.103-108
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• 2024

In this paper, a smart elevator system was studied using real-time object detection technology based on YOLO(You only look once)v5. When an external elevator button is pressed, the YOLOv5 model analyzes the camera video to determine whether there are people waiting, and if it determines that there are no people waiting, the button is automatically canceled. The study introduces an effective method of implementing object detection and communication technology through YOLOv5 and MQTT (Message Queuing Telemetry Transport) used in the Internet of Things. And using this, we implemented a smart elevator system that determines in real time whether there are people waiting. The proposed system can play the role of CCTV (closed-circuit television) while reducing unnecessary power consumption. Therefore, the proposed smart elevator system is expected to contribute to safety and security issues.

• Korean Journal of Clinical Pharmacy
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• v.34 no.2
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• pp.126-133
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• 2024

Background: Medication's benefits and harms require careful management. Laws mandate pharmacists to provide essential medication details since inadequate counseling may pose risks. This study explores public expectations for pharmacist-provided side effect information to enhance safety. Methods: A self-developed questionnaire was created for participant to self-report, refined through pilot surveys with experts and laypersons. Nineteen items were categorized into four sections, using closed-ended questions. Adults over 20, having obtained prescription medications within the past year, were surveyed via convenience sampling. Data analysis employed descriptive statistics and T-tests using IBM SPSS Statistics 21 and Microsoft Excel. Results: The study involved 189 participants, with a slightly higher proportion of females (59.3%) than males (40.7%), predominantly in their 20s (45.0%) and college graduates (57.1%). Health professionals represented 76.2% of respondents. Over half visited pharmacies at least 5 times yearly for prescriptions. Indirect experience with side effects was more common (30.2%) than direct experience (17.5%). Most (82.0%) showed interest in media-reported side effect events. Satisfaction with pharmacist-provided side effect explanations was low (59.7%), but importance was high (98.9%). Preferences favored combined verbal and written explanations (65.1%), with a majority desiring explanations for common but less serious side effects (82.5%). Healthcare professionals found explanations significantly more sufficient than non-professionals did. Older individuals, those living with elderly, and frequent pharmacy visitors attributed greater importance to pharmacist-provided explanations. Conclusion: Koreans view pharmacist-provided medication side effect explanations as vital but find current services lacking. Enhancements in content and delivery methods are needed in pharmacy counseling to meet public expectations.

• Convergence Security Journal
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• v.24 no.2
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• pp.39-45
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• 2024

The purpose of security control in the public sector is to secure the safety of administrative services for the public by preventing resource loss or information infringement in information systems and information and communication networks. The security control system is a process that performs real-time detection, analysis, response, and reporting through system vulnerability analysis and security system detection pattern optimization. This study aims to objectively identify the current situation of the mismatch between the supply and demand of cyber security control centers currently in operation and specialized security control companies that can be entrusted to operate them, and to derive and propose practical and institutional improvement measures. Considering that the operation of security control centers in the public sector is expected to increase in the future, research on the practical supplementation required for the operation process of security control centers and the improvement of the designation system of security control specialized organizations has fundamental and timely significance, and it is an area that requires continuous research in terms of strategic industrialization.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.1-10
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• 2024

The research on predicting the routes of ships, which constitute the majority of maritime transportation, can detect potential hazards at sea in advance and prevent accidents. Unlike roads, there is no distinct signal system at sea, and traffic management is challenging, making ship route prediction essential for maritime safety. However, the time intervals of the ship route datasets are irregular due to communication disruptions. This study presents a method to adjust the time intervals of data using appropriate interpolation techniques for ship route prediction. Additionally, a deep learning model for predicting ship routes has been developed. This model is an LSTM model that predicts the future GPS coordinates of ships by understanding their movement patterns through real-time route information contained in AIS data. This paper presents a data preprocessing method using linear interpolation and a suitable deep learning model for ship route prediction. The experimental results demonstrate the effectiveness of the proposed method with an MSE of 0.0131 and an Accuracy of 0.9467.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.65-72
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• 2024

Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early-stage decision-making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning-based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting-edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.551-557
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• 2024

Hydrogen is a renewable energy source with various characteristics such as clean, carbon-free and high-energy, and is internationally recognized as a "future energy". With the rapid development of the hydrogen energy industry, more hydrogen infrastructure is needed to meet the demand for hydrogen. However, hydrogen infrastructure accidents have been occurring frequently, hindering the development of the hydrogen industry. HyRAM+, developed by Sandia National Laboratories, is a software toolkit that integrates data and methods related to hydrogen safety assessments for various storage applications, including hydrogen refueling stations. HyRAM+'s physics mode simulates hydrogen leak results depending on the hydrogen refueling station components, graphing gas plume dispersion, jet frame temperature and trajectory, and radiative heat flux. In this paper, hydrogen leakage data was extracted from a hydrogen refueling station in Samcheok, Gangwon-do, using HyRAM+ software. A hydrogen leakage simulator was developed using data extracted from HyRAM+. It was implemented as a dashboard that shows the data generated by the simulator using a database and Grafana.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.311-321
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• 2024

This study comprehensively analyzed the perceptions of Chinese consumers who have and have not purchased Samsung smartphones, based on data from the social media platform Weibo. Various big data analysis techniques were used, including text mining, frequency analysis, centrality analysis, semantic network analysis, and CONCOR analysis. The results indicate that positive perceptions of Samsung smartphones include aspects such as design aesthetics, camera functionality, AI features, screen quality, specifications, and performance, and their status as a premium brand. On the other hand, negative perceptions include issues with pricing, a yellow tint in photos, slow charging speeds, and safety concerns. These findings will provide a crucial basis for making significant improvements in Samsung's market strategy in China.