• Steel and Composite Structures
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• v.43 no.5
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• pp.673-688
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• 2022

Post-earthquake fire is a major threat since most structures are designed allowing some damage during strong earthquakes, which will expose a more vulnerable structure to post-earthquake fire compared to an intact structure. A series of experimental research on steel-concrete composite beam-to-column joints subjected to fire after cyclic loading has been carried out and a clear reduction of fire resistance due to the partial damage caused by cyclic loading was observed. In this paper, by using ABAQUS a robust finite element model is developed for exploring the performance of steel-concrete composite joints in post-earthquake fire scenarios. After validation of these models with the previously conducted experimental results, a comprehensive numerical analysis is performed, allowing influential parameters affecting the post-earthquake fire behavior of the steel-concrete composite joints to be identified. Specifically, the level of pre-damage induced by cyclic loading is regraded to deteriorate mechanical and thermal properties of concrete, material properties of steel, and thickness of the fire protection layer. It is found that the ultimate temperature of the joint is affected by the load ratio while fire-resistant duration is relevant to the heating rate, both of which change due to the damage induced by the cyclic loading.

• Advances in aircraft and spacecraft science
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• v.9 no.2
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• pp.131-148
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• 2022

The paper presents the surface-modified NACA 2412 airfoil performance with variable cavity characteristics such as size, shape and orientation, by numerically investigated with the pre-validation study. The study attempts to improve the airfoil aerodynamic performance at 30 m/s with a variable angle of attack (AOA) ranging from 0° to 20° under Reynolds number (R_e) 4.4×10⁵. Through passive surface control techniques, a boundary layer control strategy has been enhanced to improve flow performance. An intense background survey has been carried out over the modifier orientation, shape, and numbers to differentiate the sub-critical and post-critical flow regimes. The wall-bounded flows along with its governing equations are investigated using Reynolds Average Navier Strokes (RANS) solver coupled with one-equational transport Spalart Allmaras model. It was observed that the aerodynamic efficiency of cavity airfoil had been improved by enhancing maximum lift to drag ratio ((l/d) max) with delayed flow separation by keeping the flow attached beyond 0.25C even at a higher angle of attack. Detailed investigation on the cavity distribution pattern reveals that cavity depth and width are essential in degrading the early flow separation characteristics. In this study, overall general performance comparison, all the cavity airfoil models have delayed stalling compared to the original airfoil.

• Journal of Marine Bioscience and Biotechnology
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• v.14 no.2
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• pp.69-75
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• 2022

The micro-Kjeldahl method, a common technique for analyzing crude proteins, is time-consuming and dangerous due to the employment of reagents such as sulfuric acid and sodium hydroxide. However, a Fourier transform near-infrared (FT-NIR) spectrophotometer analysis can be completed in under a minute after simple pre-processing if data has been gathered using sufficient reference material in advance. Furthermore, the use of safe reagents in this technique ensures the safety of the experimenter and the environment. In addition, a portable FT-NIR spectrophotometer enables real-time measurement at processing or distribution sites and has recently gained popularity. The standard errors of calibration and regression (r²) for the calibration result for estimating the crude protein content of dried laver were 0.9775 and 1.2526, respectively. The standard error of prediction was 1.1814, and the r² was 0.9303 in the validation results, which was a good level. In the present study, a method for predicting the crude protein content of dried laver using an FT-NIR spectrophotometer in the range of 29%-40% crude protein content has been reported.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.504-519
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• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.195-199
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• 2015

Many conventional management methods have emphasized the minimization of required resources along the supply chain. Accordingly, this paper presents a proposed method called the Virtual Design and Construction (VDC)-aided system. It is based on object-oriented resource control, in order to accomplish a feed-forward control monitoring supply chain logistics. The system is supported by two main parts: (1) IT-based Technologies; and (2) VDC Models. They enable the system to convey proactive information from the detection technology to its linked visualization. The paper includes a field study as the system's pre-test: the Scaffolding Works in a LNG Mega Project. The study demonstrates a system of real-time productivity monitoring by use of the RFIDbased Mobile Information Hub. The on-line 'productivity dashboard' provides an opportunity to display the continuing processes for each work-package. This research project offers the observed opportunities created by the developed system. Future work will entail research experiments aimed towards system validation.

• BMB Reports
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• v.56 no.11
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• pp.612-617
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• 2023

Pleiotropic regulator 1 (PLRG1), a highly conserved element in the spliceosome, can form a NineTeen Complex (NTC) with Prp19, SPF27, and CDC5L. This complex plays crucial roles in both pre-mRNA splicing and DNA repair processes. Here, we provide evidence that PLRG1 has a multifaceted impact on cancer cell proliferation. Comparing its expression levels in cancer and normal cells, we observed that PLRG1 was upregulated in various tumor tissues and cell lines. Knockdown of PLRG1 resulted in tumor-specific cell death. Depletion of PLRG1 had notable effects, including mitotic arrest, microtubule instability, endoplasmic reticulum (ER) stress, and accumulation of autophagy, ultimately culminating in apoptosis. Our results also demonstrated that PLRG1 downregulation contributed to DNA damage in cancer cells, which we confirmed through experimental validation as DNA repair impairment. Interestingly, when PLRG1 was decreased in normal cells, it induced G1 arrest as a self-protective mechanism, distinguishing it from effects observed in cancer cells. These results highlight multifaceted impacts of PLRG1 in cancer and underscore its potential as a novel anti-cancer strategy by selectively targeting cancer cells.

• Journal of the Korean Society of Safety
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• v.38 no.5
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• pp.51-57
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• 2023

Numerous studies have been conducted to analyze the causal relationships of maritime accidents using natural language processing techniques. However, when multiple causes and effects are associated with a single accident, the effectiveness of extracting these causal relations diminishes. To address this challenge, we compiled a dataset using verdicts from maritime accident cases in this study, analyzed their causal relations, and applied labeling considering the association information of various causes and effects. In addition, to validate the efficacy of our proposed methodology, we fine-tuned the KoELECTRA Korean language model. The results of our validation process demonstrated the ability of our approach to successfully extract multiple causal relationships from maritime accident cases.

• Journal of the Korean Society of Radiology
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• v.81 no.6
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• pp.1274-1289
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• 2020

Deep learning has recently become one of the most actively researched technologies in the field of medical imaging. The availability of sufficient data and the latest advances in algorithms are important factors that influence the development of deep learning models. However, several other factors should be considered in developing an optimal generalized deep learning model. All the steps, including data collection, labeling, and pre-processing and model training, validation, and complexity can affect the performance of deep learning models. Therefore, appropriate optimization methods should be considered for each step during the development of a deep learning model. In this review, we discuss the important factors to be considered for the optimal development of deep learning models.

• Journal of Wind Energy
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• v.14 no.4
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• pp.87-97
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• 2023

Due to the limitations of onshore wind power, the wind power industry is currently transitioning to offshore wind power. There has been active research on the development of a floating LiDAR system (FLS) that is easy to install at a low cost. The Carbon Trust published a commercialization roadmap for FLS in 2013, and an updated version was released in 2018, taking into account industry experience. The roadmap divides the development maturity of FLS into three stages: Stage 1 (prototype), Stage 2 (pre-commercialization), and Stage 3 (commercialization), each of which requires availability and accuracy assessment. The results must meet the requirements of the Key Performance Index (KPI) for each stage. Therefore, when developing FLS, the motion compensation algorithm of the FLS is essential because the LiDAR can produce incorrect measurements of wind speed and direction due to the six degrees of freedom in motion. In this study, we implemented the FLS motion compensation algorithm developed by Nassif, F.B. et al. and validated it using data provided by Fraunhofer. In conclusion, the results showed that the determination coefficients of wind speed and wind direction were improved compared to those obtained from the met mast.

• Journal of Wind Energy
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• v.15 no.1
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• pp.50-59
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• 2024

Floating LiDAR systems provide significant savings in cost and time compared to the fixed meteorological mast measurement type, and have the advantage of being able to be deployed in various locations due to less restriction on the depth of the installation site. However, to use the wind data collected by a floating LiDAR system commercially, verification procedure is required to ensure that the collected data have sufficient availability. The Carbon Trust OWA roadmap presents guidelines in three stages for the reliability of the wind data collected using a floating LiDAR system. Companies developing wind farms are requesting at least Stage 2 (pre-commercial stage) presented by OWA, and many overseas companies are leading the domestic and overseas markets. In this paper, we introduce the case of OWA Stage 2 certification for the commercial operation of floating LiDAR systems.