• The Journal of Economics, Marketing and Management
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• v.6 no.4
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• pp.1-16
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• 2018

Purpose - This study will investigate and identify the relationship between brand image, brand attitude and intent to purchase based on subjects that have remembered or watched more than one storytelling marketing ad related to airlines. The purpose of the project is to secure market competitiveness by presenting the basis for and use of the marketing strategy using storytelling, which can capture the goodwill of the aerospace competition market in the future. Research, design, data, and methodology - Prior to the research model and hypothesis testing phase, a verification factor analysis was conducted to assess internal consistency among each measurement item and to ensure reliability and validity of the measurement tool. Further, the organization was assessed for validity by calculating the mean variance estimation (AVE) and the construction concept reliability (CCR) through a positive factor analysis. Hypothesis verification was analyzed through a structural equation model, and each concept set in the hypothesis was entered as a potential variable, and each measurement item was entered as an observation variable. Results - Airline's storytelling marketing has a significant impact on the brand image and two emotional and cognitive responses have been shown to influence the brand image. In addition, airline storytelling marketing has a significant impact on brand attitudes and airline storytelling marketing derived from factor analysis has shown two emotional and cognitive responses to brand attitudes. Conclusions - The parts derived based on the research results show that storytelling marketing has a strong influence on the airline's brand image and attitude, and that it is necessary for airlines to have a brand image and attitude. Also, forming a favorable brand image has a significant impact on brand attitudes. We believe that by presenting basic data to the aviation industry in future research on airline storytelling, we will be able to increase understanding and contribution to development of storytelling marketing in aviation.

• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.226-233
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• 2023

Background: Inactive or old, healed tuberculosis (TB) on chest radiograph (CR) is often found in high TB incidence countries, and to avoid unnecessary evaluation and medication, differentiation from active TB is important. This study develops a deep learning (DL) model to estimate activity in a single chest radiographic analysis. Methods: A total of 3,824 active TB CRs from 511 individuals and 2,277 inactive TB CRs from 558 individuals were retrospectively collected. A pretrained convolutional neural network was fine-tuned to classify active and inactive TB. The model was pretrained with 8,964 pneumonia and 8,525 normal cases from the National Institute of Health (NIH) dataset. During the pretraining phase, the DL model learns the following tasks: pneumonia vs. normal, pneumonia vs. active TB, and active TB vs. normal. The performance of the DL model was validated using three external datasets. Receiver operating characteristic analyses were performed to evaluate the diagnostic performance to determine active TB by DL model and radiologists. Sensitivities and specificities for determining active TB were evaluated for both the DL model and radiologists. Results: The performance of the DL model showed area under the curve (AUC) values of 0.980 in internal validation, and 0.815 and 0.887 in external validation. The AUC values for the DL model, thoracic radiologist, and general radiologist, evaluated using one of the external validation datasets, were 0.815, 0.871, and 0.811, respectively. Conclusion: This DL-based algorithm showed potential as an effective diagnostic tool to identify TB activity, and could be useful for the follow-up of patients with inactive TB in high TB burden countries.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2628-2641
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• 2023

Nuclear safety is the lifeline for the development and application of nuclear energy. In severe accidents of pressurized water reactor (PWR), aerosols, as the main carrier of fission products, are suspended in the containment vessel, posing a potential threat of radioactive contamination caused by leakage into the environment. The gas-phase aerosols suspended in the containment will settle onto the wall or sump water through the natural deposition mechanism, thereby reducing atmospheric radioactivity. Aiming at the low accuracy of the aerosol model in the ISAA code, this paper improves the natural deposition model of aerosol in the containment. The aerosol dynamic shape factor was introduced to correct the natural deposition rate of non-spherical aerosols. Moreover, the gravity, Brownian diffusion, thermophoresis and diffusiophoresis deposition models were improved. In addition, ABCOVE, AHMED and LACE experiments were selected to validate and evaluate the improved ISAA code. According to the calculation results, the improved model can more accurately simulate the peak aerosol mass and respond to the influence of the containment pressure and temperature on the natural deposition rate of aerosols. At the same time, it can significantly improve the calculation accuracy of the residual mass of aerosols in the containment. The performance of improved ISAA can meet the requirements for analyzing the natural deposition behavior of aerosol in containment of advanced PWRs in severe accident. In the future, further optimization will be made to address the problems found in the current aerosol model.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.57-69
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• 2024

Crop classification plays a vitalrole in monitoring agricultural landscapes and enhancing food production. In this study, we explore the effectiveness of Long Short-Term Memory (LSTM) models for crop classification, focusing on distinguishing between apple and rice crops. The aim wasto overcome the challenges associatedwith finding phenology-based classification thresholds by utilizing LSTM to capture the entire Normalized Difference Vegetation Index (NDVI)trend. Our methodology involvestraining the LSTM model using a reference site and applying it to three separate three test sites. Firstly, we generated 25 NDVI imagesfrom the Sentinel-2A data. Aftersegmenting study areas, we calculated the mean NDVI values for each segment. For the reference area, employed a training approach utilizing the NDVI trend line. This trend line served as the basis for training our crop classification model. Following the training phase, we applied the trained model to three separate test sites. The results demonstrated a high overall accuracy of 0.92 and a kappa coefficient of 0.85 for the reference site. The overall accuracies for the test sites were also favorable, ranging from 0.88 to 0.92, indicating successful classification outcomes. We also found that certain phenological metrics can be less effective in crop classification therefore limitations of relying solely on phenological map thresholds and emphasizes the challenges in detecting phenology in real-time, particularly in the early stages of crops. Our study demonstrates the potential of LSTM models in crop classification tasks, showcasing their ability to capture temporal dependencies and analyze timeseriesremote sensing data.While limitations exist in capturing specific phenological events, the integration of alternative approaches holds promise for enhancing classification accuracy. By leveraging advanced techniques and considering the specific challenges of agricultural landscapes, we can continue to refine crop classification models and support agricultural management practices.

• Journal of Hydrogen and New Energy
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• v.34 no.5
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• pp.413-420
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• 2023

Holmium-doped TiO₂ nanotubes (Ho-TNTs) were manufactured through anodization treatment and electrochemical deposition, and optimization experiments were conducted using various Holmium doping concentrations and time as variables. Surface as well as electrochemical characteristics were analyzed to study the prepared photocatalysts. Ho-TNTs were found to exist only in anatase phase through X-ray diffraction analysis. Ho-TNTs with 0.01 wt% 100 seconds shows a photocurrent density of 3.788 mA/cm² and an effective photo-conversion efficiency (PCE) of 4.30%, which is more efficient than pure TiO₂ nanotubes (pure-TNTs) (at bias potential 1.5 V vs. Hg/HgO). The photocatalytic activity of the aforementioned Ho-TNTs for hydrogen production was evaluated with the result of -29.20 µmol/h·cm².

• Biomolecules & Therapeutics
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• v.31 no.6
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• pp.661-673
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• 2023

Treatment of colorectal cancer (CRC) has always been challenged by the development of resistance. We investigated the antiproliferative activity of licochalcone H (LCH), a regioisomer of licochalcone C derived from the root of Glycyrrhiza inflata, in oxaliplatin (Ox)-sensitive and -resistant CRC cells. LCH significantly inhibited cell viability and colony growth in both Ox-sensitive and Ox-resistant CRC cells. We found that LCH decreased epidermal growth factor receptor (EGFR) and AKT kinase activities and related activating signaling proteins including pEGFR and pAKT. A computational docking model indicated that LCH may interact with EGFR, AKT1, and AKT2 at the ATP-binding sites. LCH induced ROS generation and increased the expression of the ER stress markers. LCH treatment of CRC cells induced depolarization of MMP. Multi-caspase activity was induced by LCH treatment and confirmed by Z-VAD-FMK treatment. LCH increased the number of sub-G1 cells and arrested the cell cycle at the G1 phase. Taken together LCH inhibits the growth of Ox-sensitive and Ox-resistant CRC cells by targeting EGFR and AKT, and inducing ROS generation and ER stress-mediated apoptosis. Therefore, LCH could be a potential therapeutic agent for improving not only Ox-sensitive but also Ox-resistant CRC treatment.

• Journal of Surface Science and Engineering
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• v.56 no.6
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• pp.393-400
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• 2023

Acetone, a metabolite detected from the exhaled breath of people doing a diet, can be used for non-invasive monitoring of diet efficiency. Thus, gas sensors with rapid response and recovery characteristics to acetone need to be developed. Herein, we report ultrafast acetone sensors using Ce-doped In₂O₃ nanoparticles prepared by the one-pot microwave-assisted hydrothermal method. The pure In₂O₃ sensor shows a high response and fast response time (τ_res = 6 s) upon exposure to 2 ppm acetone at 300 ℃, while exhibiting a relatively sluggish recovery speed (τ_recov = 1129 s). When 20 wt% Ce is doped, the τ_recov of the sensor significantly decreased to 45 s withholding the fast-responding characteristic (τ_res = 6 s). In addition, the acetone response (resistance ratio, S) of the sensor is as high as 5.8, sufficiently high to detect breath acetone. Moreover, the sensor shows similar acetone sensing characteristics even under a highly humid condition (relative humidity of 60%) in terms of τ_res (6 s), τ_recov (47 s), and S (4.7), demonstrating its high potential in real applications. The excellent acetone sensing characteristics of Ce-doped In₂O₃ nanoparticles are discussed in terms of their size, composition, phase, and oxygen adsorption on the sensing surface.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.76-90
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• 2024

Although artificial intelligence (AI) can be utilized in various domains such as smart city, healthcare, it is limited due to concerns about the exposure of personal and sensitive information. In response, the concept of distributed machine learning has emerged, wherein learning occurs locally before training a global model, mitigating the concentration of data on a central server. However, overall learning phase in a collaborative way among multiple participants poses threats to data privacy. In this paper, we systematically analyzes recent trends in privacy protection within the realm of distributed machine learning, considering factors such as the presence of a central server, distribution environment of the training datasets, and performance variations among participants. In particular, we focus on key distributed machine learning techniques, including horizontal federated learning, vertical federated learning, and swarm learning. We examine privacy protection mechanisms within these techniques and explores potential directions for future research.

• Journal of Ginseng Research
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• v.48 no.2
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• pp.190-201
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• 2024

Background: Deposition of immune complexes drives podocyte injury acting in the initial phase of lupus nephritis (LN), a process mediated by B cell involvement. Accordingly, targeting B cell subsets represents a potential therapeutic approach for LN. Ginsenoside compound K (CK), a bioavailable component of ginseng, possesses nephritis benefits in lupus-prone mice; however, the underlying mechanisms involving B cell subpopulations remain elusive. Methods: Female MRL/lpr mice were administered CK (40 mg/kg) intragastrically for 10 weeks, followed by measurements of anti-dsDNA antibodies, inflammatory chemokines, and metabolite profiles on renal samples. Podocyte function and ultrastructure were detected. Publicly available single-cell RNA sequencing data and flow cytometry analysis were employed to investigate B cell subpopulations. Metabolomics analysis was adopted. SIRT1 and AMPK expression were analyzed by immunoblotting and immunofluorescence assays. Results: CK reduced proteinuria and protected podocyte ultrastructure in MRL/lpr mice by suppressing circulating anti-dsDNA antibodies and mitigating systemic inflammation. It activated B cell-specific SIRT1 and AMPK with Rhamnose accumulation, hindering the conversion of renal B cells into plasma cells. This cascade facilitated the resolution of local renal inflammation. CK facilitated the clearance of deposited immune complexes, thus reinstating podocyte morphology and mobility by normalizing the expression of nephrin and SYNPO. Conclusions: Our study reveals the synergistic interplay between SIRT1 and AMPK, orchestrating the restoration of renal B cell subsets. This process effectively mitigates immune complex deposition and preserves podocyte function. Accordingly, CK emerges as a promising therapeutic agent, potentially alleviating the hyperactivity of renal B cell subsets during LN.

• Journal of Ginseng Research
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• v.47 no.5
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• pp.645-653
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• 2023

Background: Changes to work-life balance has increased the incidence of cervical cancer among younger people. A minor ginseng saponin known as ginsenoside Rk1 can inhibit the growth and survival of human cancer cells; however, whether ginsenoside Rk1 inhibits HeLa cell proliferation is unknown. Methods and results: Ginsenoside Rk1 blocked HeLa cells in the G0/G1 phase in a dose-dependent manner and inhibited cell division and proliferation. Ginsenoside Rk1 markedly also activated the apoptotic signaling pathway via caspase 3, PARP, and caspase 6. In addition, ginsenoside Rk1 increased LC3B protein expression, indicating the promotion of the autophagy signaling pathway. Protein processing in the endoplasmic reticulum signaling pathway was downregulated in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, consistent with teal-time quantitative PCR and western blotting that showed YOD1, HSPA4L, DNAJC3, and HSP90AA1 expression levels were dramatically decreased in HeLa cells treated with ginsenoside Rk1, with YOD1 was the most significantly inhibited by ginsenoside Rk1 treatment. Conclusion: These findings indicate that the toxicity of ginsenoside Rk1 in HeLa cells can be explained by the inhibition of protein synthesis in the endoplasmic reticulum and enhanced apoptosis, with YOD1 acting as a potential target for cervical cancer treatment.