• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4809-4818
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• 2022

The decommissioning project of a nuclear facility is a large-scale process that is expected to take about 15 years or longer. The range of risks to be considered is large and complex, then, it is expected that various risks will arise in decision-making by area during the project. Therefore, in this study, the risk family derived from the Decommissioning Risk Management (DRiMa) project was reconstructed into a decommissioning project risk profile suitable for the Kori Unit 1. Two criteria of uncertainty and importance are considered in order to prioritize the selected 26 risks of decommissioning project. The uncertainty is scored according to the relevant laws and decommissioning plan preparation guidelines, and the project importance is scored according to the degree to which it primarily affects the triple constraints of the project. The results of risks are divided into high, medium, and low. Among them, 10 risks are identified as medium level and 16 risks are identified as low level. 10 risks, which are medium levels, are classified in five categories: End state of decommissioning project, Management of waste and materials, Decommissioning strategy and technology, Legal and regulatory framework, and Safety. This study is a preliminary assessment of the risk of the decommissioning project that could be considered in the preparation stage. Therefore, we expect that the project risks considered in this study can be used as an initial data for reevaluation by reflecting the detail project progress in future studies.

• Journal of Korean Critical Care Nursing
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• v.16 no.2
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• pp.42-53
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• 2023

Purpose : This retrospective mixed-methods study aimed to explore key considerations for designing effective simulated education in nursing, focusing specifically on the recognition and response to deteriorating patients. Methods : Quantitative and qualitative data were analyzed to assess the clinical judgment and performance of the nursing students. Descriptive statistics were used to analyze quantitative data related to prior knowledge, simulation satisfaction, clinical judgment, and nursing performance during deteriorating patient simulations. Qualitative content analysis was conducted for the reflective journal entries of the participants. Results : Quantitative analysis showed that most participants demonstrated a "being skillful" level of clinical judgment (33.1%) in effective response. At the beginner level, clinical judgment varied across effective noticing(39.7-82.8%), effective interpretating(77.6-82.8%), effective responding(3.4-86.2%), and effective reflecting(90.0-95.4%). Nursing performance in assessing patient respiration or SpO2 after request from a physician ranged from 46.6-48.3%. Qualitative analysis indicated that 48.5% of the participants anticipated a deteriorating condition and initiated appropriate actions, while 70% noticed patient unresponsiveness for the first time. Conclusion : To design an effective simulation program for identifying and addressing deteriorating patient care, a framework for observation and interpretation is essential, along with regular simulated training. It is important to design and assess simulation programs and to conduct thorough interviews with nursing students to gain insight into their clinical decision-making.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.63-74
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• 2020

The assembly of modular construction requires a series of thoroughly-considered decisions for crane lifting including the crane model selection, crane location planning, and lift path planning. Traditionally, this decision-making process is empirical and time-consuming, requiring significant human inputs. Recently, research efforts have been dedicated to improving lift planning practices by leveraging cutting-edge technologies such as automated data acquisition, Building Information Modelling (BIM) and computational algorithms. It has been demonstrated that these technologies have advanced lift planning to some degree. However, the advancements tend to be fragmented and isolated. There are two hurdles prevented a systematic improvement of lift planning practices. First, the lack of formalized lift planning workflow, outlining the procedure and necessary information. Secondly, there is also an absence of a shared information environment, enabling storages, updates and the distribution of information to stakeholders in a timely manner. Thus, this paper aims to overcome the hurdles. The study starts with a literature review in combination with document analysis, enabling the initial workflow and information flow. These were contextualised through a series of interviews with Australian practitioners in the crane-related industry, and systematically analysed and schematically validated through an expert panel. Findings included formalized workflow and corresponding information exchanges in a traditional lift planning practice via a Business Process Model and Notation (BPMN). The traditional practice is thus reviewed to identify opportunities for further enhancements. Finally, a BIM-based lift planning workflow is proposed, which integrates the scattered technologies (e.g. BIM and computational algorithms) with the aim of supporting lift planning automation. The resulting framework is setting out procedures that need to be developed and the potential obstacles towards automated lift planning are identified.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.163-168
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• 2013

A construction workspace is considered as a critical factor to secure constructability and safety of a project. Specially, optimized size of each workspace helps to minimize any conflicts between workspaces, works and resources within a workspace in the construction site. However, since an existing method for making a decision workspace's size depends on generally experiences of managers and work conditions of activity, it is difficult to perform safe works considering feasible workspace size. The workspace size is changed according to the quantity of resources allocated into each activity as time progresses. Accordingly, it is desirable that optimized workspace size considering input size of resources is determined. To solve these issues, this study configures an optimized model for deciding standard size of workspaces by simple regression analysis and develops a visualized scenario model for simulating the optimized workspace shape in order to support BIM (Building Information Modeling) environment. For this, this study determines an optimized resource shape size considering maximum working radius of each resource and constructs its visual model. Subsequently, input size of resources for each activity is estimated considering safety execution area of resources and workspaces. Based on this, an optimized 3D workspace shape is generated as a VR simulation model of a BIM system based on the suggested methodologies. Moreover, operational feasibility of the developed system is evaluated through a case study for a bride project. Therefore, this study provides a visualized framework so that project managers can establish an efficient workspace planning in BIM environment. Besides, it is expected that constructability, productivity and safety of the project will be improved by minimizing conflicts between workspace and congestions between resources within a workspace in the construction phase.

• Journal of Environmental Science International
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• v.32 no.12
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• pp.933-941
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• 2023

Since the Industrial Age, economic activities have raised environmental concerns, emphasizing the importance of biodiversity conservation areas. However, a fundamental contradiction exists between conservation and utilization, leading to conflicting interests. In light of these issues, the aim of this study was to propose efficient operational strategies for future urban biodiversity conservation areas, while also promoting local community economic development. Accordingly, the focus was the Gwangneung Forest Biosphere Reserve as a case study. The findings reveal the following. First, all local residents recognize the importance of the biosphere reserve and hold a high regard for its direct role in conservation. Second, developing and promoting brands appears to have a more positive impact on local economic activation than activating projects linked to the biosphere reserve. Simultaneously, local residents have expressed negative evaluations of indiscriminate facility development, fearing reckless expansion. Third, if governance is promoted in the future, community participation will likely increase, leading to a strengthening of conservation awareness and the establishment of a framework among local residents and those in adjacent areas. Findings of this study are expected to serve as fundamental data for establishing effective communication among local residents in protected areas facing similar challenges, thus facilitating efficient decision-making processes.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• Journal of Information Science Theory and Practice
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• v.12 no.2
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• pp.49-63
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• 2024

Information behavior played a significant role in minimizing the risks of the COVID-19 pandemic. When faced with such a situation, an individual needs information for decision-making and in order to determine the best course of action relating to their health. This study aims to explore information behavior during each phase of the COVID-19 pandemic in Indonesia, which is known for its close-knit collective culture. A sensemaking approach is used, which emphasizes the process individuals go through to understand their situation and give meaning to the information they are getting from their environment. Data was collected through in-depth interviews with 10 participants to obtain a description of their information behaviors during the pandemic. Data analysis was carried out using open, axial, and selective coding. We propose a sensemaking-based information behavior strategy framework for mitigating risk and reducing ongoing health crises. Changes in information behavior strategies, including search, prevention, and restriction of information exposure, were random at the beginning of the pandemic, but became more regular in later phases. This was influenced by the "knowledge gap fulfillment" and "use of local knowledge" among the participants throughout the pandemic. In conclusion, the participants developed a sensemaking process including an understanding of the pandemic situation and the risks that they faced. They used a number of information behavior strategies to prevent transmission, and their perception of the risks changed across the course of the pandemic, up til the situation began to be considered back to normal again in Indonesia.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.226-234
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• 2024

The frequent handover problem and playing ping-pong effects in 5G (5th Generation) ultra-dense networking cannot be effectively resolved by the conventional handover decision methods, which rely on the handover thresholds and measurement reports. For instance, millimetre-wave LANs, broadband remote association techniques, and 5G/6G organizations are instances of group of people yet to come frameworks that request greater security, lower idleness, and dependable principles and correspondence limit. One of the critical parts of 5G and 6G innovation is believed to be successful blockage the board. With further developed help quality, it empowers administrator to run many systems administration recreations on a solitary association. To guarantee load adjusting, forestall network cut disappointment, and give substitute cuts in case of blockage or cut frustration, a modern pursuing choices framework to deal with showing up network information is require. Our goal is to balance the strain on BSs while optimizing the value of the information that is transferred from satellites to BSs. Nevertheless, due to their irregular flight characteristic, some satellites frequently cannot establish a connection with Base Stations (BSs), which further complicates the joint satellite-BS connection and channel allocation. SF redistribution techniques based on Deep Reinforcement Learning (DRL) have been devised, taking into account the randomness of the data received by the terminal. In order to predict the best capacity improvements in the wireless instruments of 5G and 6G IoT networks, a hybrid algorithm for deep learning is being used in this study. To control the level of congestion within a 5G/6G network, the suggested approach is put into effect to a training set. With 0.933 accuracy and 0.067 miss rate, the suggested method produced encouraging results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1478-1499
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• 2024

Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. COVID-19 become an active epidemic disease due to its spread around the globe. The main causes of the spread are through interaction and transmission of the droplets through coughing and sneezing. The spread can be minimized by isolating the susceptible patients. However, it necessitates remote monitoring to check the breathing issues of the patient remotely to minimize the interactions for spread minimization. Thus, in this article, we offer a wearable-IoTs-centered framework for remote monitoring and recognition of the breathing pattern and abnormal breath detection for timely providing the proper oxygen level required. We propose wearable sensors accelerometer and gyroscope-based breathing time-series data acquisition, temporal features extraction, and machine learning algorithms for pattern detection and abnormality identification. The sensors provide the data through Bluetooth and receive it at the server for further processing and recognition. We collect the six breathing patterns from the twenty subjects and each pattern is recorded for about five minutes. We match prediction accuracies of all machine learning models under study (i.e. Random forest, Gradient boosting tree, Decision tree, and K-nearest neighbor. Our results show that normal breathing and Bradypnea are the most correctly recognized breathing patterns. However, in some cases, algorithm recognizes kussmaul well also. Collectively, the classification outcomes of Random Forest and Gradient Boost Trees are better than the other two algorithms.

• The Journal of Bigdata
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• v.8 no.1
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• pp.111-129
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• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.